{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 皮马印第安人糖尿病数据集分类器练习\n",
    "1、 任务描述\n",
    "请在 Pima Indians Diabetes Data Set（皮马印第安人糖尿病数据集）进行分类器练习。\n",
    "\n",
    "2、 数据说明：\n",
    "\n",
    "原始数据集地址： https://archive.ics.uci.edu/ml/datasets/Pima+Indians+Diabetes \n",
    "数据集只有一个文件（diabetes.csv）：Pima Indians Diabetes Dataset 包括根据医疗\n",
    "记录的比马印第安人 5 年内糖尿病的发病情况，这是一个两类分类问题。每个类的\n",
    "样本数目数量不均等。一共有 768 个样本，每个样本有 8 个输入变量和 1 个输出\n",
    "变量。缺失值通常用零值编码。\n",
    "\n",
    "1)  字段说明\n",
    "Pregnancies： 怀孕次数\n",
    "Glucose： 口服葡萄糖耐受试验中，2 小时的血浆葡萄糖浓度。\n",
    "BloodPressure： 舒张压（mm Hg）\n",
    "SkinThickness： 三头肌皮肤褶层厚度（mm）\n",
    "Insulin：2 小时血清胰岛素含量（μU/ ml）\n",
    "BMI： 体重指数（体重，kg /（身高，m）^ 2）\n",
    "\n",
    "2)  DiabetesPedigreeFunction： 糖尿病家族史\n",
    "\n",
    "3)  Age： 年龄（岁）\n",
    "Outcome： 输出变了/类别标签（0 或 1，出现糖尿病为 1, 否则为 0）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导入工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:11:20.599476Z",
     "start_time": "2018-10-18T05:11:16.843261Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据探索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:11:20.687481Z",
     "start_time": "2018-10-18T05:11:20.607476Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>116</td>\n",
       "      <td>74</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.6</td>\n",
       "      <td>0.201</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>78</td>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "      <td>88</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.248</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0.134</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>197</td>\n",
       "      <td>70</td>\n",
       "      <td>45</td>\n",
       "      <td>543</td>\n",
       "      <td>30.5</td>\n",
       "      <td>0.158</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8</td>\n",
       "      <td>125</td>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.232</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "5            5      116             74              0        0  25.6   \n",
       "6            3       78             50             32       88  31.0   \n",
       "7           10      115              0              0        0  35.3   \n",
       "8            2      197             70             45      543  30.5   \n",
       "9            8      125             96              0        0   0.0   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  \n",
       "5                     0.201   30        0  \n",
       "6                     0.248   26        1  \n",
       "7                     0.134   29        0  \n",
       "8                     0.158   53        1  \n",
       "9                     0.232   54        1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv(\"diabetes.csv\")\n",
    "train.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据基本信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:11:20.767486Z",
     "start_time": "2018-10-18T05:11:20.697482Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "缺失值都用0编码代替了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:11:20.903493Z",
     "start_time": "2018-10-18T05:11:20.775486Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 怀孕次数与糖尿病的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:11:21.671537Z",
     "start_time": "2018-10-18T05:11:20.910494Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Relation between Pregnancies and Diabetes  ')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4AbAoIr5Vh3ijOq72kLQp6YPq/mpiRcTnI6ItIiaQtttvIqLqo2BJQyQN7XhM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\n/x8AUNQUbfifdgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x718ac88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Pregnancies', hue = 'Outcome', data = train)\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Relation between Pregnancies and Diabetes  ')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "怀孕次数大于7次之后，得糖尿病的人数基本上都多于不得病的人"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 小时的血浆葡萄糖浓度与糖尿病的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:11:22.205568Z",
     "start_time": "2018-10-18T05:11:21.676538Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Relation between Glucose and Diabetes')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4PIqxxYyysrLm13v27HExEqUSWzAY5L777mPlV19x6cEV9LOrtbbmgiFVHJTdyH333cuy\nZcuiHKWzolbN1RizFdhqv64Qka+B3sCZwPH2aNOBecBN9vDnjNXmwEIRyRaRnvZ8Oq3du3d/53WP\nHj1cjEYB/PG6Kyjftb3D5pfVrTt3T5ra6ngTJkxg1qxZ5OfnN7f9o5xhjGHq1KnMnTuXswZU84OQ\nWkut8XngykP2cucXXm6eeBOPTJnKoEGDohitcxx5DkJE+gGHAYuA7k0HfWPMVhHJt0frDWwOmazY\nHpYwCaK0tNTFSFST8l3buWngqg6b331rIxvvoosu4oorruBXv/pVhy1bRWbatGm8+uqrjOtTw+l9\nwzfE98LqNADOH/L9iiWZyYYbRpZx5+fCDddfx8OPTKGwsDCqMTsh6jepRSQDmAlcbYzZu79Rwwz7\nXgtmInKpiCwWkcU7d+7sqDBdE7oOmiAS249//GNyclp/6lp1rOeff57p06czpmct5w6qZl9tJG6q\n9LGpct/n1N38QW4cuYdATTnXXH0VxcXFUYrYOVFNECKShJUcXjTGvGoP3i4iPe3PewI77OHFQJ+Q\nyQuAkpbzNMY8aYwZbYwZnZeXF73gHVJaWgpp2SDCrl273A5HqYTy8ssv8/TTT3Ns9zouGVqFp50N\n6PZKDzJxZBl1lXu45uqrKCn53iEsrkSzFpMATwNfG2MeDPnodeBC+/WFwGshw39l12Y6Gijv7Pcf\nAHbt2kWjLw1JTvtOcZNSKrpefvllnnjiCY7Kr+M3B1e2Ozk0KcgIcNPIMmr2lnLVlX+I6yQRzSuI\nHwIXACeKyFL771TgXmCsiKwBxtrvAd4C1gFFwD+A30Uxtpixc1cpJimVoC9Vi5iUcsgrr7zSnBwu\nH1b5vSel26tvZoAbR5ZRXV7K1VddGbdPW0ezFtMCwt9XAPhJmPEN8PtoxROr9paXY7rmEfDVslur\nuSoVda+++iqPPvooR+ZFJzk06ZcZ4KZRZdy7FK69+ioenjKV/Pz81ieMIdqaq4vq6uqor6/D+PwY\nn5+ysvLWJ1JRl9Wte8Q1jyKdXyTOPfdc5s2bx65duygoKOC2227jkksu6bhAFLNmzeKRRx7hiG71\n/HZ49JJDk372lcR9y+Caq6/ikSlTyc3Nje5CO5AmCBc1NbNhfMkYbzJV2uxGTIjkmYVoeOmll1xZ\nbqKYN28ekyY9wIjcBn5/SAU+hxoaGtAlwPUjyrl/Kc1VYDMzM51ZeDtpW0wuqqqy2nkxXitB1NRU\nh+2bWCnVPl988QV33nEHg7Ia+cMhex1LDk0GZzVy5SF72bhxA3/6483U10f+IJ6bNEG4qLa21nrh\n8YHHhzEmbnYcpeLFxo0b+fMtfyLf38i1h+4lxetOHIfmNnDZwRUsX/El999/f1ycDGqCcFFTgjAe\nH8ZjlfZpg33uiIcfazzEGGsqKyv5480T8TRWc92IMtKT3N2GR3ev56wB1cyZM4cZM2a4GkskNEG4\nqPlqweO1/tAE4Qa/309paWlMH4CNMZSWluL3+90OJW4YY7jv3nvZtnUrVw4vJy81NjrnOr1vDUfn\n1/H0U0/xxRdfuB3OfulNahc1JQgjXoydIBoaGtwMKSEVFBRQXFxMrDfd4vf7KSgocDuMuPGf//yH\nDxcs4NxBVQzJjp0OfUTg4qGVbKxK5o7bb+PZadPJyspyO6ywNEG4qLkXKo8XRBOEW5KSkujfv7/b\nYagOVFJSwhNPPM6I3AbG96l1O5zvSfXB74eV89fFwpQpU7jlllvcDiksLWJyUVMyMOIFj+c7w5RS\nB8YYw4OTHsATrOfigyr32fie2wozA5zRz7ofsWjRIrfDCUsThIu+vQfhsZIEmiCUaq+FCxeyeMnn\n/KJfFbn+2LjvsC+n962he5rhsalTYrJfa00QLmpOBuIF0SsIpdorGAzy9ycep0e64Se9Y69oqSWf\nB84ZWMHGzcW88847bofzPZogXNR8k1prMSnVIRYuXMiGjZv4Wd9Kxx+GO1CHd2ugf5cAL780g0Ag\nsm5OnRInm7Bzak4GHm/zcxD6oJxSB+6fL79Mt1Ta1GWo20Tg1D7VFG8pYeHChW6H8x2aIFxUW1tr\n7R3ixXiSAKipCd/doVJq/0pKSli2fDnH96yOm6uHJqPz6slKgbfffsvtUL4jzjZj51JVVYX4kq0k\n4U1qHqaUars5c+YgwA97xF8xrdcDx+bX8Mknn1BRUeF2OM00QbiooqICvMmA1aJr8zClVJt99NEC\nBmQFYr7m0r4cmV9PIBDk008/dTuUZpogXLR7926CgUZSNi0Ejw/xJbNHOw1Sqs3Kysr45pvVjMqJ\nv6uHJgO6NJKZTEw9E6EJwkXbtu/AmCCeaqsvapOcxo4dO1yOSqn4s2LFCgAO7hq/1cQ9AkOz6li+\nbKnboTTTBOGSYDDItm1bQb5t7aQxKZPNxVtcjEqp+LRy5Up8HuiXGXsPm7XF4KxGtm3fETP902uC\ncMmWLVsINDY2N9IHEEzNonjz5ph8olKpWFZUtIaCjCDJLvX10FGaElxRUZHLkVg0Qbhk9erV1gu7\n9hJAIC2XxsYG1q9f71JUSsWndUVFFKRF79mHF1ansbHCy8YKL3d/3oUXVqdFZTl9MqwH5datWxeV\n+beVJgiXLF++HPEmNT8gBxDIyAe+LU9VSrWuqqqK3WXl9EqP3lPImyp91AQ81AQ8rCpLYlNldBrC\nTk8ydEkRNm/eHJX5t5UmCJd8tngxDRndCW1q0qRkgj+TJUuWuBiZUvFlyxbrvl33GOkQqL26+xso\nLi52OwxAE4QrNm7cSMmWLTRmfb/zl/rM3nz22WJtk0mpCG3duhWAvNTYasfoQOWnBti2tcTtMABN\nEK744IMPAGjMLvzeZ41d+1JfXxdTD8soFcu2bdsGQLc4fUCupW7+ALt2lcZEZRVNEA4zxvDue+8R\nzOyOScn43ueBzJ5IcirvvfeeC9EpFX+2b99Oqk9I98Vun+Jt0c0fJGhMTHSBqwnCYV9//TXFmzdT\nnzso/AgeD3VdB/DxJ59QVlbmbHBKxaHt27eT6w/EbM9xbdXUVEjTlZGbNEE47PXXX0e8STTkDNjn\nOA15Qwg0NsZkByJKxZqSLcXk+d0vjukoeX7rXoomiARTXl7O3LlzqcsZ8J3nH1oKpnYlmNmD//vP\nf2KuAxGlYokxhpKtW8nvJDeowbqC8Mi3tbPcpAnCQbNmzaKhoYGG/GGtjluXfzDbt23jk08+cSAy\npeLTzp07qaurp2da50kQPg/kpxk2bdrkdiiaIJxSX1/Pv2fOJNClF8G0rq2O39i1L6Rk8PI//+lA\ndErFpw0bNgB0qgQB0DO1gQ3r3X+aWhOEQ+bOncue3bup63FoZBOIh9r84Xy5YgUrV66MbnBKxak1\na9YAUJjRuRJEYUYjxcVbXO9hMmoJQkSeEZEdIvJlyLBbRWSLiCy1/04N+exmESkSkW9EZFy04nJD\nIBDghRdfxKTnEujSK+LpGvKGIEkpzJgxI4rRKRW/vvnmG/LSrCYqOpN+mY0EjXG90b5oXkFMA8aH\nGT7ZGDPK/nsLQESGAecAw+1pHhOROG+X8VsLFixgS3ExtT0OpU118bxJ1OYdzEcffRQzjXcpFSuM\nMaxYvozBmZ2v1YHBWVatLLfbZYtagjDGfADsjnD0M4GXjTF1xpj1QBHwg2jF5iRjDNOmT4fULBq7\n9mvz9PXdhyHeJF544YWOD06pOLZ582b2lJVzUHb8dhK0L12SDb3SDcuWutt5kBv3IK4QkeV2EVTT\n3dreQGjzhcX2sLj30UcfsX7dOmp6jAA5gM3t81ObN5T333+fjRs3dnyASsWppuZoDsnpfAkCYFjX\nWpYu/cLVdtmcThCPAwOBUcBWYJI9PFy5S9hCRRG5VEQWi8jiWHgUfX+MMTw7bRr4u9CYO/CA59PQ\n4xDweHn++ec7Ljil4tzCTz6hR7ohr5O04trSyJwG6uobWOriVYSjCcIYs90YEzDGBIF/8G0xUjHQ\nJ2TUAiBsc4bGmCeNMaONMaPz8vKiG3A7ffzxx6wtKqKm58gDu3qwmaRUavMOZu7cuXoVoRRQUVHB\nF0u/4IjcWrdDiZqDuzbg9wkffvihazE4miBEpGfI258DTTWcXgfOEZEUEekPDAbiujnTYDDIU08/\n3e6rhybWVYSP6dOnd0B0SsW3jz76iEAgyOj86PUi57ZkL4zMqeXDD+a71rJrNKu5vgR8AhwkIsUi\ncglwv4isEJHlwAnANQDGmJXAv4CvgHeA3xtj4rpi84cffmjde+g5ql1XD02ariL++/77WqNJJbw5\nc2aTl2YYkNl52mAK5+judZTvrXCtE7Fo1mI61xjT0xiTZIwpMMY8bYy5wBhzqDFmhDHmDGPM1pDx\n7zLGDDTGHGSMeTtacTkhEAjw1FNPY1Kzaczdd6N8bVXf81DEm8QzzzzTYfNUKt6Ulpby+ZLPOSav\nptO04LovI3IbSEuC2bNnu7J8fZI6CubOncvmzZuo7XVYh1w9NPOlUJs/nAULFrBq1aqOm69ScWT2\n7NkEjeGHPTrf8w8tJXngqLxaPvzwA6qrqx1fviaIDlZfX29dPaR3O6DnHlqdf4/hSJKfJ598ssPn\nrVSsM8bw7jtvMzArQM/0zll7qaUf9ayjrq6e+fPnO75sTRAd7LXXXmPHju3U9D6ibU9NR8qbTE2P\nkXz++efaLalKOGvWrGH9ho2M6eFsG0U1jYLf7+ess87C7/dT0+hc2dagLo30SDe8/fZbji2ziSaI\nDlRWVsaz06YRyOpNICt6z/k15A8FfxemTJ0aE/3WKuWUd955xyp2cbj2UnWjcNppp3HFFVfw05/+\nlGoHE4QIjOlezfLlKygpCVv7P2o0QXSgJ598kurqamr7RLmVEI+XmoIj2bxpEzNnzozuspSKEY2N\njfx3zmwO61bneON8aT7DrFmzmDJlCm+++SZpDvd/fUwPKyHOmTPH0eVqguggS5cu5a233qKu+yEE\nU1vv76G9GrMLaczuw9NPP8PWrVtbn0CpOLdkyRLK9lZwTHfnb06n+gy1tbXMnDmT2tpaUh1OEN38\nQYZmNzL7vXcxxrlla4LoAJWVldx99z3g70J9r8OcWagItYXH0BAIcvc992jXpKrTmz9/PqlJVtXP\nRPSD/Fo2F29p7iTJCZog2skYw4MPPsiOnTuo6v9j8PqcW3ZKBtWFR7Ni+XJefPFFx5arlNOCwSAf\nL/iQkTl1JCXoUWt0nlXMtGDBAseWmaCbuuPMnDmT//73v9T1OoxgRr7jy2/MHURDzgCeefZZFi1a\n5PjylXLC2rVrKdtbwcicztu0RmuyUwx9M4MsWbzYsWVqgmiHJUuW8Nhjj9HYtZD6niPdCUKE2n4/\nxKTlcOttt8VER+dKdbSmFk0PzknsWnvDutaxcuWX1Nc7kyg1QRygdevWccstfybgz6am/3HReeYh\nUt4kqgaeSG2j4YYbb2TPnj3uxaJUFBQVFZHth5yUxHg4bl/6ZzbS0Bhw7ERQE8QB2LFjBzfceBO1\nQaFq0EngTXI7JExKJpUDf8KOnbu4aeJE1zs7V6ojbVi/joLUxC1ealKQblVGcarZf00QbVRRUcEN\nN97I7rJyKgedhEnJcDukZsGMfKr6H8/q1av566236kN0qtMo27OHrAS/egCat0FZWZkjy9ME0Qb1\n9fX88Y9/YuOmTVQNPJFgWm675peyaSHe6lK81aWkrnqLlE0L2x1joGshtYXH8OmiRUyaNMnROtNK\nRUtlVZXjD6fFonR7G1RUVDiyPOfqZMY5Ywz33XcfK1Ysp2bAcQS69Gr3PD3Vu5GAVafbV7GNjjrf\nb8gfijRU8/bbb9OrVy8uuOCCDpqzUu5ITk6iMdjJ2/aOQIN9EZWcnOzI8vQKIkLTp09n7ty51PU+\nokN6iIu2+l6H0ZA7kKeffpr333/f7XCUapf09AyqHGz/KFZVNVqH7PT0dEeWpwkiAgsWLGDatGk0\n5A6ivucIt8OJjAi1/X5EMLM799xzL0VFRW5HpNQB61PYl5Jq9yuDuK2kygtAnz59HFmeJohWbNmy\nhTvvuotgejdq+x3rbnXWtvJ4qR54AvXi40+33EJVVZXbESl1QAYOHEhJlYfaBK93sX6vlSAGDOi4\nnir3RxPEfjQ0NHDb7bdT1xCkeuCJ4Im/WzYmKY3q/sezfft2HnzwQb1preLS6NGjCRpYsduZsvdY\ntbQ0hcGDBpKdne3I8iJOECLDXNxFAAAbx0lEQVTSV0ROsl+nikhm9MKKDdOnT2f1N99Q3feHMVWd\nta0Cmd2p63UYc+fOdby5YKU6wiGHHEKXzAwW7UjcBLGr1kNRuY8fjfmxY8uMKEGIyG+AfwN/twcV\nAP+JVlCx4Ouvv+bFF1+kodtgGnP6uR1Ou9X3HEEwI5/JDz3Ezp073Q5HqTbx+XyMP+VUFu9MYXdt\nYhZ8zC32IyKMGzfOsWVGuqV/D/wQ2AtgjFkDON8ynUPq6uq4++57MMnp1PY5yu1wOoZ4qO4/hpra\nOh54QJ+PUPHn5z//OSC8s9nvdiiOq2oQ3t+ayg9/9CN69Ojh2HIjTRB1xpjm59xFxAd02iPMc889\nx+bNm6jueyz4Os8lrfFnUdPrcBYtWsjs2bPdDkepNunZsycnjxvHnC2p7Kxx9iqiMKORVG+QVG+Q\nodkNFGY4e7f8jY2p1DTChRde6OhyI93K80Xkj0CqiIwFXgHeiF5Y7vnmm2946aWXaOg2mEBWgdvh\ndLiG7sMIZubz8MOPUFpa6nY4SrXJhAkT8PqSmFGUjpMXwecPqaZvZoC+mQH+ePhezh9S7diyS6o8\nzC5OZezYkxk0aJBjy4XIE8REYCewArgMeAu4JVpBuaWhoYF77rkX4/NHv19pt4iHmr4/orqmhsmT\nJ2tRk4or+fn5XHjRxSzZmcynCXDDOmjgqVVd8Kelc9lllzm+/IgShDEmaIz5hzHmbOBSYJHphEeW\nGTNmsGHDeqoKjwVfitvhRE0wNZvaXoexYMEC5s+f73Y4SrXJL3/5Sw46aAjT12RS2slvWL+50U9R\nuZc/XHkVubnta/vtQERai2meiHQRkRxgKfCsiDwY3dCctWHDBp577jkacgYQ6FrodjhRV9/jEEx6\nNyY/9JBjDX8p1RF8Ph+33PJngl4/j67sQmMnbeT1qz0+/r0+nRNPPIGxY8e6EkOk6TfLGLMX+AXw\nrDHmCOCk6IXlLGMMkydPJujxUVfYSWottUY8VPf7IeXl5Tz11FNuR6NUm/Tp04cbb5pIUbmX51Y7\nez/CCTtqPDz2VRZ9Cgq4/vobEJdacIg0QfhEpCfwS2BWFONxxfvvv8+yZcuo6XUEJinV7XAcE0zL\npT7vYF57/XXWrFnjdjhKtcnxxx/P+eefz7wSP29u6jxVXysbhEnLszFJ6dx5192kpaW5FkukCeJ2\n4F1grTHmMxEZAHSKI0ogEODpZ57BpOXQkDfE4YXX4/f7Oeuss/D7/RBwvsesut6HId5knp02zfFl\nK9VeEyZM4MQTT+Rfa9OZXxL/9w1rG2Hy8ix21fm46+57KCx0t7g70pvUrxhjRhhjfmu/X2eM+Z/o\nhuaMDz/8kC3FxdT2HAXi7A0vaazntNNO44orruCnP/0p0uhCl4q+FGq7D+fjjz5i3bp1zi9fqXbw\neDxMnDiR0aOP4JlVGXyyLX5rNtUHYPKKLNZWJPHnv/yVESPcbzk60pvUBSLyfyKyQ0S2i8hMEekU\nDwm8++67kJJBY9e+ji/b+JKZNWsWU6ZM4c0338S49FBeff5QEI8+PKfiUnJyMnfeeRcjRozg719n\n8nEcJom6AExe0YVVZT5uvvlmfvxj59pb2p9IT5mfBV4HegG9sR6SezZaQTmltraWTz/9lPrsvu40\n4+1Npra2lpkzZ1JbWwtel3Zsn5/GLj2ZN/8Dd5avVDv5/X7uufdeDj10BH//KpMP4qi4qaZReGBZ\nFl/vSWbixJtdq7EUTqQJIs8Y86wxptH+mwbk7W8CEXnGvuL4MmRYjojMFpE19v+u9nARkUdEpEhE\nlovI4Qe8Rm1QUlJCIBAgkL7fVUkIgfQ8tm0toaGhwe1QlDogaWlp3Hf//Rwx+gieWpURF2027a0X\n7l2aRVFFMn/+y18cbYgvEpEmiF0icr6IeO2/84HW2mmYBoxvMWwiMNcYMxiYa78HOAUYbP9dCjwe\nYVzt0tSqaTDZme77YplJTscYw65du9wORakD5vf7ueuuuxkzZgwz1qTzytrUmK0Cu6vGw51fdGVL\njZ877riTE044we2QvifSBDEBq4rrNmArcJY9bJ+MMR8Au1sMPhOYbr+eDvwsZPhzxrIQyLar1UZV\nU7+u4kLtoZhjb4OMjPjt90IpgJSUFG699VZOO+003tiYxtOr0gnE2MN0myq93PFFVypJ54FJkzj2\n2GPdDimsiLpIM8ZsAs7ogOV1N8Zstee5VUSamgzvDWwOGa/YHra15QxE5FKsq4x2VwHLz7cW76kt\nJ4AzfbzGKk9NOf7UVE0QqlPwer1cd9115OTk8Nxzz1Fe7+WKQ/aS4nU7MusJ6Ye/zCK9Sw6T/vaA\nY92HHohIazFNF5HskPddReSZDowj3B3isBeGxpgnjTGjjTGj8/Lad+8gPz+ffv0HkFy2oV3ziXsm\nSEr5Jo495hjXnthUqqOJCBMmTODaa69lxe5k7vkim4p6d/fvT3ck88CyLPJ79uGxx5+I6eQAkRcx\njTDGlDW9McbsAQ47gOVtbyo6sv/vsIcXw3dO4QuAkgOYf5udPPYkPBU78FTuaH3kTspXuhbTUMtP\nfvITt0NRqsOdccYZ3Hb77RTX+Lnji66O9yXRZHaxn0e/zGTowcOYMvXR5hKMWBbplvI01TgCqzYS\nERZPtfA60NTjxYXAayHDf2XXZjoaKG8qioq2n/3sZ2R37Urq5k+J2btZ0RRoIG3LEg4aOjRmy0GV\naq8xY8bwwKRJVJLOHV90pbjSubImY+DVdak8vzqdY449lkkPTqZLly6OLb89Ik0Qk4CPReQOEbkD\n+Bi4f38TiMhLwCfAQSJSLCKXAPcCY0VkDTDWfg9W/xLrgCLgH8Dv2rwmBygtLY3LL7sMT+UOkrcu\nc2qxscEY/Bs+goYarvzDH7R4SXVqI0aM4JEpU/GmZnP30mzW7Y1+kggaeGFNGv/ZkMb48eO5/fbb\nSUmJn2c0Ir1J/ZyILAZOxLpf8AtjzFetTHPuPj76XjmG3bfE7yOJJRrGjRvHZ599xty5cwmkd+uU\nPcmFk7TjK5J2r2PCJZcwfPhwt8NRKuoGDBjAI1Mf5fprr+G+pXD9yHIGZ0Wn+9CggWnfpDOvxM/Z\nZ5/Nb3/7Wzye+Oq/ItKb1IVAJVZR0GtApT2sUxARrrvuOvr1H0D62vfxVGx3O6So85Wuxb9pEccc\ncwznnXee2+Eo5ZjevXvzyNRHycnvyQPLslhTfiCl5fsXmhzOP/98fve738VdcoDIi5jexGrmexbW\nA27rgLejFZQb0tLSmPTA3+jRPZ+MotmO3LQOpuVgvEkYbxKNmT0IpuVEfZkAvt3rSF3/ASNHjuTW\nW2+Nyx1XqfbIy8vjoYcfsZLE8iw2VnRccZMxMGNNWnNyuOSSS+K2+DbS1lwPtVtzHWE/Bf0DYEF0\nQ3Nebm4uDz80me55uWSsfhdv2ebWJ2qHusKjCaTlEkjLpWboqdQVHh3V5QEkbf+K1LXzGD58OPfc\nc09clYcq1ZHy8vKY/NDDZGT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wNEGoUE+6HYBS+6D7pgv0HoRSSqmw9ApCKaVUWJoglFJK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\nVFiaIJRSSoX1/wHJjHqohY63JwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x146f57f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x = 'Outcome', y = 'Glucose', data = train, hue = 'Outcome')\n",
    "plt.legend(loc = 'upper center')\n",
    "plt.title('Relation between Glucose and Diabetes')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "得糖尿病的人，其2小时后血浆葡萄糖深度是会比较高，所以这也才是糖尿病。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 舒张压与糖尿病的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:11:23.137621Z",
     "start_time": "2018-10-18T05:11:22.210568Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x14a5bda0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PBc45twm4E9hApLjVAO8B1c65tjsOy4GBXQ0pIiKSjJxzbwHbd1l9GpEDmLDzgczTgMdd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14a5bd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax0, ax1) = plt.subplots(1,2,figsize = (15,5))\n",
    "sns.distplot(train['BloodPressure'],kde = False, ax = ax0)\n",
    "sns.violinplot(x = 'Outcome', y ='BloodPressure', hue='Outcome', data = train, ax = ax1)\n",
    "plt.legend(loc = 'upper center')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "看起来跟舒张压没有很大的联系，因为正常与偏高的都有"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 三头肌皮肤褶层厚度与糖尿病的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:11:23.632649Z",
     "start_time": "2018-10-18T05:11:23.144622Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Relation between SkinThickness and Diabetes')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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b2tejKCOxgvGH59IispgxY+7m888/ty0Wf9V0BrEETw+mK/FUKS3kzx5Mi4Mb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\nI1ei+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14a69588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x = 'Outcome', y = 'SkinThickness', hue = 'Outcome', data = train)\n",
    "plt.legend(loc = 'upper center')\n",
    "plt.title('Relation between SkinThickness and Diabetes')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "三头肌皮肤褶层厚度比较大的基本上都有糖尿病，而且右边糖尿病这边的数据的离群点也还是比较大的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2 小时血清胰岛素含量与糖尿病的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:11:24.675709Z",
     "start_time": "2018-10-18T05:11:23.638650Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x146f54e0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9f061/aLznJWkXq3fMsnZV93OyZd9mbOvut3RxDXrNKxujYgf0BoFeFtErACe6OB1fwb8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\nSSqeYSVJKt7/B1LEeaYBAwdwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x146f5550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x = 'Outcome', y = 'Insulin', data = train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:11:25.158737Z",
     "start_time": "2018-10-18T05:11:24.703711Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x173c0c50>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7kVzX1NTELbfcwm233cYJ/cJ879h6T+4F0d75w1q4fFyQ+QsW8O1vf4uNGzd6\nW1AHFBCHqK6ujttuvx1XUkGkr/eX0LdUTWDHju3cc889Xpci4rk1a9Zwzb9/g3889xxfGN7Ed45p\nID9LlkH6bFUL3/9EPduq3+eaf/8GM2fOxLnMTbvtCgXEIdi9ezffveEG9tbV0zT8DMjz/j9jtGwQ\n4QFH8cQTT3D//fdn7S+cSDo1NTUxdepUvnXttQR3b+HGE+q4eHQzfu//RD/ihH6t/PrkWsaUNnHr\nrbfy/e99LytbE5mbtN9DrFixgl//5jfs2LmbprGfJVaSPZfPh4adgsUiTJs2je3bt3Pdddd1uxuU\niBwK5xyvvPIKd/7hDmpr9/DpyhYuHtVESSB7Pyj1KXD8x3H1vLylgOnvvsXVV13Fly+9lMsvv5zC\nwkKvywMUEJ1WX1/Pn//85/h00sIyguPOJVY64PAOGg1TWFjIBRdcwMyZM2mMHuagleXRMuJMYoFi\nnp89m4WLFnPDd7/D2WefjaVxbSgRL23cuJE/3HEHy5YvZ3hZjOtPamB0eTeZJWRwdmWICf3DPLyu\nmAcffJB/vvgC13/nu5x55pme/90qIDpQX1/PE088wfTHHqO5uZnwoGMJDTkefId/0xGLhLlg8gVc\nf/31OOeY/uzswy/YjHDVSUT6jCC2cQH/8z//wyOPPsqVX/86p512mue/cCKp0tzczAMPPMBj06eT\nnxflq2ODnFPZgi/LupM6o1e+45vjg/zL4BAPrI3xs5/9jJNPnsANN3yPqqoqz+pSQBzAnj17ePzx\nx3niySdpaW6mtfdwwiNPIFbcN2XncP78fQNUs2bNwvlTd0PzWEkFwaMuILBrLWurV/CTn/yE0aPH\ncMUVlzNx4kTysmDcRORQrVixgpv+9zds2bqNiYNauGRME+X52dud1FlH9onw3xP28M/NhTz15lKu\nvupKrvnmtXzxi1/05G/WuvNg5oQJE1yql5jYtm0bjz76KDNnzaI1HKa1z0jCQ45LaTC0KVr9HP6G\nbfueR8oG0XzkpJSfh1gMf+37FG17C5rrqayq4qtf+Qqf+cxnCAQyf/tFkUMVDoe59957mT79UfoV\nOf79iHqOzMC6Sr9Z3ovVez/8Wzmydys/ObE+refcEzLuW13GW7sDHH/ccdz44x8zaNCglBzbzJY5\n5yZ0tJ9aEAl1dXXcc889zJr1HDHnaK0YTXjQsZ5eAJcyeXlE+o2loWI0/toN1Gx/m5tvvpl77r2P\nb137Tc455xx1PUnWC4VC/PznP+O115bw6SEtXDomSFEPfgfrU+D4wSfqmbe1gAffeYvrvv0tbr/j\nDwwdOjRjNWRVP4OZnWdma8wkE2o6AAAMYElEQVRsnZndmIlzxmIxZs2axVe++lWenTmTln7jaDx2\nCi0jJ/aMcGjP8ohUjKLxqMk0jf0sO5sdv/rVr/je97+flVPsRNqEQiF+9tOf8tprS7jyiEauPLJn\nh0MbM/iXISF+fuJeWoN7+N53v0N1dXXGzp81AWFmPuCPwPnAeOAyMxuf7vP+/e9/57e//S31FBMc\nfyGh4afhCkrTfVpvmRHtPZTgURfQMvx0VqxcxTXXfJOtW7d6XZlIUs888wxLXn+dq45s5NOVIa/L\nybiq0ig/Pn4vrU17+d2tt2bsvFkTEMAngXXOuQ+cc2HgEeDCdJ5w69atPPDA32jtM5LgEZPSMs6Q\n1SyP1gFH0jB+MuFIRPe4lqz14guzGdkryllDvAmH5ohRWFjIlClTKCwspDmS+S7ZypIonxnSxIq3\nV7Bjx46MnDObAqIS2NTueU1i20eY2TVmttTMlu7cufOwTrh27VoikdZ4V1IO98E7fyGxQDEr33lX\nV2BL1olEIqxb9z6jyzK/CmubpohxwQXxKemf//znafIgIABG94rgXHwpkUzIpoBI9l/8Y+9Wzrm7\nnXMTnHMT+vfvf1gnnDhxIp/+9Kcp2PIG+TXLsNYcW+jOOXwN2yh+7wV84UZ+/rOfarBaso7f7+eU\nU07h9V2FRDy6cWKx3zFz5kzuvPNOZs2aRbHfmw9Si7bnU1hQwAknnJCR82XTME8N0H54vgrYks4T\nmhk/+tGPiEajzJs3j8Lt7xCqGE144DG4ovJ0nhqAWHFfIvs9zwgXw79nIwXbV5LXuJPSsjKu/8//\n5KSTTsrM+UW66EtTprBo8WKmrizjW0c3ZHxV1iN6t8LeRubNms5Av4s/z7DZmwpZsK2QCy86n9LS\nzIyTZs11EGbmB94DzgE2A68D/+ace+dAr0nldRDV1dVMnz6d55+fTSTSiiupIFw+jEif4cSK+nT/\nLqhoBH/9Zvx7NpJfX4NrbWHQ4MFc+uUvc95552XN2i8iB/Lkk08y9c47GV4W4YZj6zN+21CvRGLw\n8LpiXqwpYuKZZ/L/fvrTw/577ex1EFkTEABmNgm4HfAB9znnfn2w/dNxoVxtbS0vvvgi8+a9yrvv\nvhPvky/sRbh8KJHeQ4mWDvTk1qKHwlqb8NVtxr+nmvyGzbhohOKSEs44/XTOOussTj31VHy+7vFv\nEQFYuHAhv/zlf2HRVi4YFuS8oc1Zs4x3qjkHb+4O8PC6MrY1GZdccgnf/OY3U/I32y0DoqvSERDt\n1dbWsnDhQl599VWWLltGNBLBfAHCZYOJllcRKa/EFWTRaqmxGL7gDnx1NeTXb8aCuwHoW1HBpyZO\n5Mwzz+T444/H78+mnkWRrqmpqeFPd93F/AULqCiCS0Y2cMrAMHndvJHf3sYGHw+/X8q7tX6GVlXy\n7euu57TTTkvZ8RUQKdbU1MQbb7zBkiVLWLhoMTt3bI//oKg34fIqIr2HES0dAJbhcf9IC/69Nfj3\nVpPfsAUXCZPn83H00Udz6imn8MlPfpIxY8Zo8Fl6nDfeeIM/Tr2Tde9/wKASx6ShQc4YFCKQTVNv\nusA5WLPXz8zqYlbsDlBWWsKVV13N5MmTU/6hTgGRRs45Nm3axJIlS1i0aBFvvvkm0WgUCxQS7lVJ\npPcwIuVVKVnxNRlrqcO/p5pA3SZ8jdvBOXr36csZp5/GKaecwoknnpixQSwRL0WjUV599VUeevDv\nvLd2Hb0L4dzKIJ+uDHk206irYg6W78pnVnUx79f56F3eiykXX8KFF16Ytvu5KCAyKBgM8vrrr7Nw\n4UIWLFxEsLEh3hXVexit/cYRLRt0+IPckRCB2g/I372OvMb49R8jR41i4plncvrppzNu3Dit0Co5\nyznHsmXLeOihB1m+/A0K/TBxUDOfq2phYHF2DmY3R4x5Wwt4cXMxO5qMwYMGcull/8Z5551HQUFB\nWs+tgPBIJBJh5cqVzJkzh3/OmUNzUxMUlhHqO5rWAeNxga7NPvA1bCOwfRWBumqIRRkxYiSTJp3P\npz71qZSt7CjSk7z33ns89thjzH3pJaLRKCf0C3Pe0GaO6B3JismIO5vzeKGmkHnbimhuhWOOHs+U\niy/hzDPPzNj4oAIiC4RCIebPn88/nn+epUuXYr4ALQOOJjzomA67n/KCuynYvAx/XQ2lZWWc+7nP\ncd5552k8QaSTdu3axYwZM3hmxtPU1TcwqleUScOamNDfmwHtDQ0+nqsuYsmOAiwvj7PO+jRTpkzh\nqKOOyngtCogss2HDBu655x7mz5+PBQqJ5R9kjMDFsKZaSkpK+epXv8IXv/hFXacgcohCoRCzZ8/m\n0UceZvOWrQwodpxfFWTi4FDap8g6B+/sCTCruoh3agMUFxXyhckX8qUvfYkBAw7zlsWHQQGRpVat\nWsUTTzxBMBg86H7jxo1jypQpaRukEsk10WiUBQsW8NBDD7J69RoqiuCi4Y2cOSiUltuUvrfXz+Mf\nlLB6r5+KPr2ZcsmX+cIXvpAVE0gUECIiSTjnWL58Off85W5WrV7DoBLHv45o5JQB4ZSMUVQ3+njs\n/RLe2h2gT+9yLr/ia1xwwQXk5+cf/sFTRAEhInIQzjkWLlzIvX/5Cx9s2MAxfVu58ohG+hcd2qyn\ncBSeWl/MPzYVUVJSwmX/Fu8eLioqSnHlh08BISLSCbFYjGeeeYY//+kuYq0hvjQyyOeGtnRpIHv1\nHj/3runF9iZj0qRJXHvttfTq1St9RR8m3ZNaRKQT8vLyuOiiizj99NP5/e9/x0OLX2NtXYBrxndu\n1dh/1hTwt7WlDBo4kN//6keceOKJ6S86Q9SCEBFJcM7x2GOPcddd/8fIsijnD2066LjEu3sCvLS5\nkNNOPZWf/fznFBcXZ67Yw6AWhIhIF5nFV02trKzkf/77v/njOx03IS6++GKuvfbaHrkysgJCRGQ/\nZ5xxBo9On05tbe1B9yssLGTw4MEZqirzFBAiIkmUl5dTXp7+O0tmM63uJiIiSSkgREQkKQWEiIgk\npYAQEZGkFBAiIpKUAkJERJJSQIiISFLdeqkNM9sJbPS6jh6kH7DL6yJEktDvZmoNd87172inbh0Q\nklpmtrQz67OIZJp+N72hLiYREUlKASEiIkkpIKS9u70uQOQA9LvpAY1BiIhIUmpBiIhIUgoIwczO\nM7M1ZrbOzG70uh6RNmZ2n5ntMLOVXteSixQQOc7MfMAfgfOB8cBlZjbe26pE9vkrcJ7XReQqBYR8\nEljnnPvAORcGHgEu9LgmEQCcc/OAg9/WTdJGASGVwKZ2z2sS20QkxykgxJJs09Q2EVFACDXA0HbP\nq4AtHtUiIllEASGvA2PNbKSZ5QOXAs94XJOIZAEFRI5zzkWA64HZwCpgunPuHW+rEokzs4eBRcAR\nZlZjZld7XVMu0ZXUIiKSlFoQIiKSlAJCRESSUkCIiEhSCggREUlKASEiIkkpICTnmVmVmc0ws7Vm\n9r6Z3ZG4JuRgr/lJpuoT8YoCQnKamRnwJPC0c24sMA4oBX7dwUsVENLjKSAk150NtDjn7gdwzkWB\n7wNXmdm3zWxq245mNtPMzjKzm4AiM3vTzB5M/OwKM1thZm+Z2d8S24ab2ZzE9jlmNiyx/a9mdpeZ\nzTWzD8zsXxL3PVhlZn9td77PmdkiM1tuZo+ZWWnG/quIoIAQORpY1n6Dc64eqAb8yV7gnLsRaHbO\nHe+c+4qZHQ38P+Bs59xxwA2JXacCDzjnPgE8CPyh3WH6EA+n7wPPArclajnWzI43s37AT4HPOOdO\nBJYCP0jFP1iks5L+AYjkECP56rUH2p7M2cDjzrldAM65tvsXnAb8a+Lx34Bb2r3mWeecM7O3ge3O\nubcBzOwdYATxRRPHAwvivWDkE19yQiRjFBCS694BvtR+g5n1Ir7CbR0fbWUXHuAYnQ2T9vuEEt9j\n7R63PfcDUeBF59xlnTiuSFqoi0ly3Ryg2MyugH23YP0d8VtdfgAcb2Z5ZjaU+N332rSaWaDdMS4x\ns4rEMfomti8kvjouwFeA+V2oazFwhpmNSRyz2MzGdfUfJ3I4FBCS01x8tcovAheb2VrgPaCF+Cyl\nBcB64G3gVmB5u5feDawwswcTq9/+GnjFzN4Cfp/Y57vAlWa2AricD8cmOlPXTuDrwMOJ1y8GjjzU\nf6fIodBqriIikpRaECIikpQCQkREklJAiIhIUgoIERFJSgEhIiJJKSBERCQpBYSIiCSlgBARkaT+\nPyNuLyF9PSeCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x173162e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x = 'Outcome', y = 'Insulin', hue = 'Outcome', data = train)\n",
    "plt.legend(loc = 'upper center')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "糖尿病患者与非糖尿病患者的2 小时血清胰岛素含量差别不大。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 体重指数与糖尿病的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:11:25.625763Z",
     "start_time": "2018-10-18T05:11:25.164737Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x175c3588>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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t6z4jItnRlgAi4gPOo26E8ufAZdHDrgPmWFVDvYqKCoq/3+a68QOHC6d2pKLi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\nUsrlNAiUUsrl/n+RmB4xop7VZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5eda710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x = 'Outcome', y = 'BMI', hue = 'Outcome', data = train)\n",
    "plt.legend(loc = 'upper center')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "糖尿病患者的体重相对偏大一些，"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 糖尿病家族史与糖尿病的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:11:26.112791Z",
     "start_time": "2018-10-18T05:11:25.631764Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x176415f8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JyspyKryoC/uJJCIuIAOYCHwD+E9gbGhWszkItkazMfGrZPFiBuUG6JG277iXI3o24fcH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x391b0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x = 'Outcome', y = 'DiabetesPedigreeFunction', hue = 'Outcome', data = train)\n",
    "plt.legend(loc = 'upper center')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "是否有糖尿病家族史与得糖尿病关系不是很明显。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 年龄与糖尿病的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:11:27.754885Z",
     "start_time": "2018-10-18T05:11:26.119792Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Relation between Age and Diabetes  ')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AgULPIwAAAAC6hEcAAAAAdAmPAAAAAOgSHgEAAADQJTwCAAAAoEt4BAAAAECX\n8AgAAACALuERAAAAAF3CIwAAAAC6hEcAAAAAdAmPAAAAAOgSHgEAAADQJTwCAAAAoEt4BAAAAECX\n8AgAAACALuERAAAAAF3CIwAAAAC6hEcAAAAAdAmPAAAAAOgSHgEAAADQJTwCAAAAoEt4BAAAAECX\n8AgAAACALuERAAAAAF3CIwAAAAC6hEcAAAAAdAmPAAAAAOgSHgEAAADQJTwCAAAAoEt4BAAAAECX\n8AgAAACALuERAAAAAF3CIwAAAAC61qx0Aay8DZu3Lrh9x5ZNU6oEAAAAWG30PAIAAACgS3gEAAAA\nQJfwCAAAAIAu4REAAAAAXcIjAAAAALqWFB5V1YVLWQcAAADA/mXNQhur6tAkd09yVFXdM0mNm45I\n8r0Trg0AAACAFbZgeJTkvyR5foagaEduD4/+LcnrJ1gXAAAAAKvAguFRa+21SV5bVc9rrb1uSjUB\nAAAAsEos1vMoSdJae11V/fsk62e/prW2dUJ1AQAAALAKLCk8qqq3JvmBJBcluXVc3ZIIjwAAAAD2\nY0sKj5JsTPKA1lqbZDEAAAAArC4HLbHdZ5McO8lCAAAAAFh9ltrz6Kgkn6+qTyb59szK1tpPTaQq\nAAAAAFaFpYZHZ02yCAAAAABWp6Xebe3Dky4EAAAAgNVnqXdbuyHD3dWS5K5JDk7yjdbaEZMqDAAA\nAICVt9SeR4fPfl5VT07yIxOpCAAAAIBVY6l3W7uD1tp7kjx6L9cCAAAAwCqz1GFrPz3r6UFJNub2\nYWwAAAAA7KeWere1/zjr71uSXJ7k1L1eDQAAAACrylLnPPqFSRcCAAAAwOqzpDmPquqEqjqvqq6t\nqmuq6t1VdcKkiwMAAABgZS11wuw/S/LeJN+b5PgkfzOuAwAAAGA/ttTwaG1r7c9aa7eMy1uSrJ1g\nXQAAAACsAkudMPu6qvq5JO8Ynz8jyb9MpqT9w4bNWxfcvmPLpilVAgAAALDnltrz6P9N8rQkVye5\nKslTkphEGwAAAGA/t9SeR7+f5LTW2teTpKruleQPM4RKAAAAAOynltrz6N/NBEdJ0lr71yQPnkxJ\nAAAAAKwWSw2PDqqqe848GXseLbXXEgAAAAD7qKUGQK9O8o9V9a4kLcP8Ry9b6AVVdWiSjyQ5ZDzO\nu1prL6mqeyf5iyT3SvLpJM9qrX1nD+sHAAAAYIKW1POotbY1yc8kuSbJriQ/3Vp76yIv+3aSR7fW\nHpjkQUlOqaqHJXllkj9qrd03ydeTnL6nxQMAAAAwWUseetZa+3ySz+9G+5bkxvHpwePSkjw6yTPH\n9ecmOSvJG5a6XwAAAACmZ6nCVz3/AAAeP0lEQVRzHu2RqrpLVV2U5NokH0jyz0mub63dMja5Isnx\nndc+u6q2V9X2Xbt2TbJMAAAAADomGh611m5trT0oyQlJfiTJ/edr1nntm1prG1trG9euXTvJMgEA\nAADomGh4NKO1dn2SbUkeluTIqpoZLndCkq9NowYAAAAAdt/EwqOqWltVR45/3y3JY5NckuRDSZ4y\nNjstyfmTqgEAAACA5VnyhNl74Lgk51bVXTKEVO9srf1tVX0+yV9U1UuTfCbJmydYAwAAAADLMLHw\nqLX2v5M8eJ71X8ow/xEAAAAAq9xU5jwCAAAAYN8kPAIAAACgS3gEAAAAQJfwCAAAAIAu4REAAAAA\nXcIjAAAAALqERwAAAAB0CY8AAAAA6BIeAQAAANAlPAIAAACgS3gEAAAAQJfwCAAAAIAu4REAAAAA\nXcIjAAAAALqERwAAAAB0CY8AAAAA6BIeAQAAANAlPAIAAACgS3gEAAAAQJfwCAAAAIAu4REAAAAA\nXcIjAAAAALqERwAAAAB0CY8AAAAA6BIeAQAAANAlPAIAAACgS3gEAAAAQJfwCAAAAIAu4REAAAAA\nXcIjAAAAALqERwAAAAB0CY8AAAAA6BIeAQAAANAlPAIAAACgS3gEAAAAQJfwCAAAAIAu4REAAAAA\nXWtWugD2Txs2b120zY4tm6ZQyeL2pVoBAABg2vQ8AgAAAKBLeAQAAABAl/AIAAAAgC7hEQAAAABd\nwiMAAAAAuoRHAAAAAHQJjwAAAADoWrPSBcDu2LB564Lbd2zZNKVKAAAA4MCg5xEAAAAAXcIjAAAA\nALqERwAAAAB0CY8AAAAA6BIeAQAAANAlPAIAAACgS3gEAAAAQJfwCAAAAIAu4REAAAAAXcIjAAAA\nALqERwAAAAB0CY8AAAAA6BIeAQAAANAlPAIAAACgS3gEAAAAQJfwCAAAAIAu4REAAAAAXcIjAAAA\nALqERwAAAAB0CY8AAAAA6BIeAQAAANAlPAIAAACgS3gEAAAAQJfwCAAAAIAu4REAAAAAXcIjAAAA\nALqERwAAAAB0CY8AAAAA6BIeAQAAANAlPAIAAACga2LhUVV9X1V9qKouqarPVdWvj+vvVVUfqKpL\nx8d7TqoGAAAAAJZnkj2Pbknym621+yd5WJLnVtUDkpyR5MLW2n2TXDg+BwAAAGAVmlh41Fq7qrX2\n6fHvG5JckuT4JKcmOXdsdm6SJ0+qBgAAAACWZypzHlXV+iQPTvKJJMe01q5KhoApydGd1zy7qrZX\n1fZdu3ZNo0wAAAAA5ph4eFRVhyV5d5Lnt9b+bamva629qbW2sbW2ce3atZMrEAAAAICuiYZHVXVw\nhuDoba21vx5XX1NVx43bj0ty7SRrAAAAAGDPTfJua5XkzUkuaa2dM2vTe5OcNv59WpLzJ1UDAAAA\nAMuzZoL7fniSZyW5uKouGte9KMkrkryzqk5PsjPJUydYAwAAAADLMLHwqLX20STV2fyYSR0XAAAA\ngL1nKndbAwAAAGDfJDwCAAAAoEt4BAAAAECX8AgAAACALuERAAAAAF3CIwAAAAC6hEcAAAAAdAmP\nAAAAAOgSHgEAAADQJTwCAAAAoEt4BAAAAECX8AgAAACALuERAAAAAF3CIwAAAAC6hEcAAAAAdAmP\nAAAAAOgSHgEAAADQJTwCAAAAoEt4BAAAAECX8AgAAACALuERAAAAAF3CIwAAAAC6hEcAAAAAdAmP\nAAAAAOgSHgEAAADQJTwCAAAAoEt4BAAAAECX8AgAAACArjUrXQDsrzZs3rrg9h1bNk2pEgAAANhz\neh4BAAAA0CU8AgAAAKBLeAQAAABAl/AIAAAAgC7hEQAAAABdwiMAAAAAuoRHAAAAAHStWekCgKXb\nsHnrom12bNk0hUoAAAA4UOh5BAAAAECX8AgAAACALuERAAAAAF3CIwAAAAC6hEcAAAAAdAmPAAAA\nAOgSHgEAAADQJTwCAAAAoGvNShfA6rfz7JMXbbPuzIunUAkAAAAwbXoeAQAAANAlPAIAAACgS3gE\nAAAAQJfwCAAAAIAu4REAAAAAXcIjAAAAALqERwAAAAB0rVnpAuBAtfPskxdts+7Mi6dQyeL2pVrZ\nfRs2b120zY4tm6ZQyYFnse+W7xUAAKuBnkcAAAAAdAmPAAAAAOgSHgEAAADQJTwCAAAAoEt4BAAA\nAECX8AgAAACALuERAAAAAF3CIwAAAAC6hEcAAAAAdAmPAAAAAOgSHgEAAADQJTwCAAAAoEt4BAAA\nAECX8AgAAACALuERAAAAAF3CIwAAAAC61qx0AfuCnWefvGibdWdePIVKYDI2bN664PbzDp9SIcCi\nFvu+7tiyaSLHXezfQv8OAgDsv/Q8AgAAAKBLeAQAAABAl/AIAAAAgC7hEQAAAABdwiMAAAAAuiYW\nHlXVf6+qa6vqs7PW3auqPlBVl46P95zU8QEAAABYvkn2PHpLklPmrDsjyYWttfsmuXB8DgAAAMAq\nNbHwqLX2kST/Omf1qUnOHf8+N8mTJ3V8AAAAAJZvzZSPd0xr7aokaa1dVVVH9xpW1bOTPDtJ1q1b\nN6XyYH47zz55we3rzrx4SpWwmMU+q+TOn9eGzVsXfc2OLZv2uKZp2pP3v1L2pVoBAOBAtmonzG6t\nvam1trG1tnHt2rUrXQ4AAADAAWna4dE1VXVckoyP1075+AAAAADshmmHR+9Nctr492lJzp/y8QEA\nAADYDRMLj6rqHUk+nuR+VXVFVZ2e5BVJHldVlyZ53PgcAAAAgFVqYhNmt9ae0dn0mEkdEwAAAIC9\na9VOmA0AAADAyhMeAQAAANA1sWFrAIvZefbJC25fd+bFU6qEfcWGzVsXbbNjy6YpVLK4pdR63uFT\nKAQAAJZJzyMAAAAAuoRHAAAAAHQJjwAAAADoEh4BAAAA0CU8AgAAAKBLeAQAAABAl/AIAAAAgC7h\nEQAAAABda1a6APaunWefvGibdWdePIVKFrdYrXtS5770/gGYjEn8+wIAcCDT8wgAAACALuERAAAA\nAF3CIwAAAAC6hEcAAAAAdAmPAAAAAOgSHgEAAADQJTwCAAAAoGvNShdwoNp59smLtll35sVTqAQm\nY8PmrYu2Oe/wvb/fPdknLHZd7diyaUqVLN/+/u/LpP7bspj9/byyuMWuAZ8/APszPY8AAAAA6BIe\nAQAAANAlPAIAAACgS3gEAAAAQJfwCAAAAIAu4REAAAAAXcIjAAAAALqERwAAAAB0rVnpAoC9a+fZ\nJy+4fd2ZF0+pEiZhw+atC24/7/DJHHex6yq587W1UrVOwp68f/Yt+9P1CgCwt+l5BAAAAECX8AgA\nAACALuERAAAAAF3CIwAAAAC6hEcAAAAAdAmPAAAAAOgSHgEAAADQtWalC9jbdp598oLb15158ZQq\nAQ5kGzZvXbTNeYdvWbSN/2axP1vs3+zEd2B/N4lrwHUFAHufnkcAAAAAdAmPAAAAAOgSHgEAAADQ\nJTwCAAAAoEt4BAAAAECX8AgAAACALuERAAAAAF3CIwAAAAC61qx0AQD7gp1nn7zg9nVnXjylSoD9\nye7+t2XD5q2L7nPHlk3LqonpWezzT/bs35f9/d+sxb4He/Id8N3ad/isYGXoeQQAAABAl/AIAAAA\ngC7hEQAAAABdwiMAAAAAuoRHAAAAAHQJjwAAAADoEh4BAAAA0LVmpQsAgL1p59knL7h93ZkXT6kS\nNmzeuuD28w6fUiEs22Lfq+TO363FPv9kz64B19VkLHZed2zZNKVKVq/V8u/LUr5bPq/VwWfF/kTP\nIwAAAAC6hEcAAAAAdAmPAAAAAOgSHgEAAADQJTwCAAAAoEt4BAAAAECX8Ij/v737D7atrOs4/v5y\nLyAIgiIgCHK1BDVJE1PxRxKUIRWKosGomMo42ZCaQelYiBCTektNJzNTRNQ0f4QhqYA/MG0U5Pe9\nCBcxr4iAhqnEOOkQT3+s58L2sJ+193qedc7hcN6vmTP37LX2+dzvXmt993n2s9faR5IkSZIkqcjJ\nI0mSJEmSJBWtXe4CJEnS8jrghDNm3ufMHZegkHuYlbRdZ9V6d6lTK+u4msd1J+/fu/5BJ25YokqW\nx6zHD3Xb4O6yXWc/t6yfmXFPPwZqLMZ2nee55eL1x8y8z1JYSbXek3jmkSRJkiRJkoqcPJIkSZIk\nSVKRk0eSJEmSJEkqcvJIkiRJkiRJRU4eSZIkSZIkqcjJI0mSJEmSJBU5eSRJkiRJkqSitctdgCRJ\nksZz3cn7z7zPg07csASVrBwHnHDGzPucueP4uTWZy6XmuFqs7TrL3akHVsoxMN++Wj/zPiv5uWXW\nNrh4/TFLVMnd16zemrb/70nbdbGeB2u2wXJsV888kiRJkiRJUpGTR5IkSZIkSSpy8kiSJEmSJElF\nTh5JkiRJkiSpyMkjSZIkSZIkFS3L5FFEHBoRmyLi2oh49XLUIEmSJEmSpNmWfPIoItYAfwc8HXgE\ncHREPGKp65AkSZIkSdJsy3Hm0eOAa1NK/5lS+hnwYeAZy1CHJEmSJEmSZoiU0tL+hxFHAoemlI7N\nt18APD6ldNyC+70UeGm+uR+wac7/4v7AzSOVu9IyFyvXWq3VWldOrav98S9WrrVa62qvdbU//sXK\ntVZrXe21rvbHv1i51mqtQzL3SSntOutOa9vqqRJTlt1lBiul9C7gXYPDIy5KKT22prCVnrlYudZq\nrda6cmpd7Y9/sXKt1VpXe62r/fEvVq61Wutqr3W1P/7FyrVWa12MzOW4bO16YO+J23sBNyxDHZIk\nSZIkSZphOSaPvgY8NCIeHBHbAEcBZy1DHZIkSZIkSZphyS9bSyndFhHHAecAa4DTUkpXjvhfDL7U\n7R6UuVi51mqt1rpyal3tj3+xcq3VWld7rav98S9WrrVa62qvdbU//sXKtVZrHT1zyT8wW5IkSZIk\nSSvHcly2JkmSJEmSpBXCySNJkiRJkiQVrdjJo4jYOyK+EBFXRcSVEfGKvPw5+fbtETHoT9P1ZK6P\niKsj4oqIODMidh4p95SceVlEnBsRe7ZmTqw/PiJSRNx/pFpPiojv5lovi4jDxqg1Iv4oIjbl5W8a\nqdZ/nqhzc0RcNkLmoyPiqznzooh43Ei1PioivhIRGyLikxFxnwGZ94qICyPi8pz5+rz8wRFxQUR8\nI2+LbQbWWso9LiKurTyuSpkfzPt/Y0ScFhFbj5T7nrzsioj4WETs0Jo5sf7tEXHrSHWeHhHfmjhe\nHz1SbkTEqRFxTT7mXj5C5pcm6rwhIj4xUq2HRMQlOffLEfGLI2QenDM3RsT7IqLq8/0iYk1EXBoR\nZ+fbTb1VyKzuq57Mpr7qya3uq1LmxPLBfTWj1qbeKmRW99WM3KbeKmRW99WM3Kbeiu538oZc10V5\n2f0i4rzcV+dFxH0r6pyWWz0e7MlsGg/25FaPB0uZE+uqxoM9tVaPB/tqjYbxYE+t1ePBnsym8WBP\nbvV4MP/8ztE9L1+dn58ObO2tQmZTX/Xktr7WmpbZ1Fel3Il1ta+1ptXa1Fd9tbb0VqHWpr7qyW19\nrTUts7Wv9pt4rJdFxC0R8cqW3urJbO2BP877eGNEfCi6MXLzGPMuUkor8gvYA3hM/n5H4BrgEcDD\ngf2A84HHjpT5NGBtXv5G4I0j5d5n4j4vB97Zmplv7033geTfBu4/Uq0nAcePvK9+HfgssG1et9sY\nuQvu8zfAiSPUei7w9Lz8MOD8kbbB14Cn5uUvBk4ZkBnADvn7rYELgCcAHwGOysvfCbxsYK2l3F8B\n1gGbK46rUuZheV0AHxqx1sneejPw6tbMfPuxwPuBW0eq83TgyCFZc+a+CDgD2Cqvm7u3+h7/xH0+\nDhwzUq3XAA/Py/8QOL0x84nAd4B98/KTgZdUbt9XAf8EnJ1vN/VWIbO6r3oym/qqJ7e6r0qZeVlV\nX82otam3CpnVfTVrG0ysG9xbhVqr+6qUS/eGY1NvTTvOgTdtOZaAVzNwjNWTWz0e7MlsGg/25FaP\nB0uZeXn1eLCn1pOoHA/2ZDaNB/u2wcT6QePBnlqbxoM9udXjwfwz7wOOzd9vA+zc2luFzKa+6slt\nfa01LbOpr0q5+fuW11rTam3qq57c1tdaUx//xPrBfdVTa+trrWmZTX21IH8NcBOwT2tvFTKrewB4\nIPAtYLt8+yPA7zPCGHPh14o98yildGNK6ZL8/f8AVwEPTCldlVLaNHLmuSml2/LdvgrsNVLuLRN3\nuzeQWjPz6rcAfzokb87cKj2ZLwPekFL6aV73/TFrjYgAnkv34qk1MwFbZqt3Am4Yqdb9gH/PdzsP\nePaAzJRS2vJO/db5KwEHAx/Ly98HPHNgrVNzU0qXppQ2D8maI/NTeV0CLmR4b5Vyb4E7joHtGNZb\nUzMjYg2wnq63BunZV016cl8GnJxSuj3fb+7emlVrROxId4wNOjuiJ7e6twqZ/wf8NKV0TV4+qK+2\niIi9gN8G3p1vB429tTAzP4bqvurJbOqrntzqvipltvRVX26rQmZ1X83I3bKuqrcKmU2/swq5uzBC\nb03xDLp+goq+KmkZD/ZkNo0He3Krx4MzVI8Hl1jTeHCWmvFgj+beKqgeD+azKX4NeA9ASulnKaUf\n0dBbpczWvurJre6tnsymvurZrlDZWzMyq/XkVvfWrFpr+6ont7q3ejKr+2qKQ4BvppS+zXi/t+7I\nHOH3y1pgu+jOCN4euKF1jDnNip08mhQR6+hm1i5YgswXA58eKze6U+C/AzwPOLE1MyIOB76bUrq8\ntsZSrcBx+VS604acnteTuS/wlOguA/liRPzqiLUCPAX4XkrpGyNkvhJYn/fVXwOvGanWjcDhedVz\n6N7JGJK1Jp8u+n26J8VvAj+aePK5norJv4W5KaXm3urLjO6ymhcAnxkrNyLeSzeb/zDg7SNkHgec\nlVK6cWiNfXUCp+a+ektEbDtS7i8Av5dP+/10RDx0pFoBjgA+t2BQ1pJ7LPCpiLie7hh4Q0sm3WTJ\n1nHn6fRHMrCvsrfSDQxvz7d3ob23FmaOoZjZ0lel3Ja+KmQ29VVPLrT11rTMpr6aUSvU99a0zKa+\nKuTeTHtvJeDciLg4Il6al+2+Zf/nf3erqHVabqtZmbXjwam5jePBu2SONB4sbYOW8eC0zDHGg337\nq3Y8OC1zjPHgtNyW8eBDgP8C3hvdZabvjoh709ZbpcxW8+QO7a1iZmNfTc1t7K2+x9/SV6Xclt6a\nta9q+6qU29Jbpcym11kLHMWdE2Vj/N5amDlpUA+klL5Lt82uA24EfpxSOreyppn/2Yr+AnYALgae\ntWD5+dSfTlnKfC1wJhBj5uZ1rwFe35JJN8t4AbBTXreZ+ssgfq5WYHe6U+u2Ak4FThshcyPwNrpL\nKx5Hd7rd4G3bs7/+HviTkR7/24Bn5++fC3x2pNyH0Z2meTHwOuAHlbk7A1+geyK/dmL53sCGmswF\nuY+cWFZ9XPVk/iPw1trMntw1wDuAFzVm/hrwZe48nbTl8po76qS7nDGAbeneuRh86m8h99Ytx35+\nbvjSiNv001t6YaRa/wV4fF5+AvDuETIPBL5EN5H0l8ClA7N+B3hH/v4gukt2dm3prWmZC9YP7qs5\nMqv6ao7cwX1V2KZ7tvZVqdaW3urJbOqrObbr4N7qqbWpr3pyW3trz/zvbsDldM+tP1pwnx9WHAd3\nyZ1Ydz51l631ZVaPB/ty8/LB48HCdm0eDxZym8aDhczm8eCM/VU1HizU2jweLORWjwfpLv29baLn\n/xY4paW3SpkT62v7albu4N6alZmX1fTVtNz1Lb3Vs69a+6qUW91bc+yr2r4q1VrdWz2ZY73O2obu\nDZTd8+0xfm/9XGZjD9wX+DzdWHVrujOYnz+xftBx2vt/jRGyXF9545wDvGrKutontamZwAuBrwDb\nj11rXr8PsLElE9if7t33zfnrNroZyAeMXOu61lrzss8AB03c/iaw60j7ay3wPWCvMfYV8OMtTUz3\nBHzLIhwD+wIX1hxf+edfR/ci4WbufDF2IHBObeZE7vETt5ufgCYz8/efIH+OyJi15mVPZcpniwzM\nfB3d2RZbeut2JiYSRqrzoJY6J3OBq4F1eVnQvQMxxr7aBfgBcK+R9tUJdKfrbln2IODrI2/XpwEf\nGZjzV3RnFm3O+/0nwAdbequQ+YGJ9YP7qi+zpa9m1ZrvM6ivCpk/bO2rOWsd1FulzNa+mrG/qnqr\nkPlvrX0153Yd3FsLfv4kuuerTcAeedkewKbazMncidvnU/lm4rRMGseDfbXmZYPHg1My/4IRxoNz\n1LpuhFqPZ4TxYM/+qh4PFmptHg/OsV0HjQeBBwCbJ24/JT8PVPdWKXPidlVf9eXW9tasWvOymtdZ\n03I/19Jbc9Y6uK96joHq3pqxr1peZ5Vqre6tObdr9essusvUzp243fx7a2FmXlbbA88B3jNx+xjy\nG0D59mZW+2ceRUTQXdd4VUrpzYuZGRGHAn8GHJ5S+smIuZOnvB9ONzCtzkwpbUgp7ZZSWpdSWkc3\n8HtMSummEWrdY+JuR9DNZDdl0r2wOTjfZ1/unIFtzQX4DeDqlNL18+bNyLyB7sUSueZBp2j2bNfd\n8r9bAX9O9yG882buGvmT+CNiO7rHfBXd2RdH5ru9EPjXgbVOy5372BySGRHHAr8FHJ3y54iMkLsp\n8l8Wytv9d4fUX8i8OKX0gIne+klKachfBSs9/j0m6nwmA/qqL5eJ3qI7bq+ZnjAoE7pfTmenlP53\nSJ09uVcBO+X+B/jNvKyp1om+2pbuuXvuvgJIKb0mpbRX3tdHAZ9PKT2Pht4qZD5/SF3zZrb21bRc\n4AUtfVWo9b4tfdWT+/yW3urZV9V9NSMXKnursK+eQUNf9dXa0lv5co8dt3xPN/m0ETiLrp+g7ndW\nKbdaKXOE8WApt2U8OC3zayOMB0u1towHS/uqdTzYdwzUjgdLma3jwdJ2rR4P5v36nYjYLy86BPg6\nDb3Vk9mklNvSWz2Z1X3Vk3tJS2/11FrdV325NPTWjGOgqq9m5Fb3Vs92re6rBY7m5y8va/q9NS2z\n8ffLdcATImL7PO45hIG/9+c2xgzUcnwBT6a7ZvgK4LL8dRhdw10P/JRuRnTIO8OlzGvp/sLIlmVD\n/wpGKffjdE8OVwCfpPsQ7abMBffZzPB3sku1vh/YkJefRZ5tbczchu7d3I3AJcDBY9Sa150O/MGI\nx9WT6U55vJzudNUDRsp9Bd0LkGvoPpNiyCmKvwxcmjM3ki/LoLvu98J83H6U/BcWRsh9ee6t2+ie\n4Oe+DKIn8za6d0G2bJOhfwnlLrl0p/z+Rz5eN9KdMXKf1loX3GfoX1srPf7PT9T5AfJfDhshd2e6\nd3E20L2D8agxHj/dO42HDu2rGbUekeu8POc/ZITM9XS/NDcBr6ypd+L/OIg7L9lp6q1CZnVf9WQ2\n9dW03Na+KtW6YHn15aBTtkFTbxUyq/tq1jZo6a1CrdV9NSO3urdy/1yev64EXpuX70L3bv438r/3\nGym3ZTxYymwdD5ZyW8aDUzMX3Gczw8eDpVpbxoOlzNbxYHEbUD8eLNXaOh4s5VaPB/PPPxq4KO+X\nT9BdxtLaW9Myq/tqRm5rb03LrO6rvtwRemtardV9NSO3tbemPv7avppRa2tvTcts6qucuz3dmcE7\nTSxr7a1pma098Hq6CdKN+XjalpHGmJNfW04NkyRJkiRJku5ixV62JkmSJEmSpMXn5JEkSZIkSZKK\nnDySJEmSJElSkZNHkiRJkiRJKnLySJIkSZIkSUVOHkmSJA0QEUdERIqIhy13LZIkSUvBySNJkqRh\njga+DBy13IVIkiQtBSePJEmS5hQROwBPAl5CnjyKiK0i4h0RcWVEnB0Rn4qII/O6AyLiixFxcUSc\nExF7LGP5kiRJVZw8kiRJmt8zgc+klK4B/jsiHgM8C1gH7A8cCxwIEBFbA28HjkwpHQCcBpy6HEVL\nkiS1WLvcBUiSJK0gRwNvzd9/ON/eGvhoSul24KaI+EJevx/wSOC8iABYA9y4tOVKkiS1c/JIkiRp\nDhGxC3Aw8MiISHSTQQk4s/QjwJUppQOXqERJkqRF4WVrkiRJ8zkSOCOltE9KaV1KaW/gW8DNwLPz\nZx/tDhyU778J2DUi7riMLSJ+aTkKlyRJauHkkSRJ0nyO5q5nGX0c2BO4HtgI/ANwAfDjlNLP6Cac\n3hgRlwOXAU9cunIlSZLGESml5a5BkiRpRYuIHVJKt+ZL2y4EnpRSumm565IkSRqDn3kkSZLU7uyI\n2BnYBjjFiSNJknRP4plHkiRJkiRJKvIzjyRJkiRJklTk5JEkSZIkSZKKnDySJEmSJElSkZNHkiRJ\nkiRJKnLySJIkSZIkSUX/D4STLYQofRgUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x175caf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# setting params\n",
    "params = {'legend.fontsize': 'x-large',\n",
    "          'axes.titlesize':'xx-large',\n",
    "          'figure.figsize': (20, 10),}\n",
    "plt.rcParams.update(params)\n",
    "\n",
    "sns.countplot(x = 'Age', hue = 'Outcome', data = train)\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Relation between Age and Diabetes  ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "年龄段小的人相对得糖尿病的机率要比较小一些。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 各特征之间的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:11:28.572932Z",
     "start_time": "2018-10-18T05:11:27.759886Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1783aef0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lkiRJap+VUkmSpI4bYeGXSq2USpIkqXVWSiVJkjrOm+dLkiRJQ2ClVJIkqeNc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\nLoPSiIiIiKgug9KIiIiIqO7/Ab59APGU+p+HAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17855470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# setting params\n",
    "params = {'legend.fontsize': 'x-large',\n",
    "          'figure.figsize': (10, 10),}\n",
    "plt.rcParams.update(params)\n",
    "sns.heatmap(train.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "看热力图，舒张压、皮肤褶层厚度以及怀孕的次数的关系比较大。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 特征工程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 缺失值处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "因为题目有描述缺失值用0值来填补了，所以我们先统计一下有多少缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:11:28.603934Z",
     "start_time": "2018-10-18T05:11:28.577932Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pregnancies                 111\n",
      "Glucose                       5\n",
      "BloodPressure                35\n",
      "SkinThickness               227\n",
      "Insulin                     374\n",
      "BMI                          11\n",
      "DiabetesPedigreeFunction      0\n",
      "Age                           0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#用一个临时变量来存放train，统计除了输出Y的0值个数，因为Y=0，代表的是没糖尿病，而不是缺失值\n",
    "mid_train = train.drop('Outcome', axis = 1)\n",
    "mid_train = mid_train.replace(0, np.NaN)\n",
    "print(mid_train.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "缺失值很多，而实际上怀孕次数有可能是0，所以这个不能确定0究竟是缺失值还是怀孕次数是0次，而上面的怀孕次数与糖尿病的关系分析中看到，怀孕次数对糖尿病是有一些影响的，怀孕次数0次或1次的得病概率相对较低，所以我们还是先不作处理。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:11:28.730941Z",
     "start_time": "2018-10-18T05:11:28.633936Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Glucose                     0\n",
      "BloodPressure               0\n",
      "SkinThickness               0\n",
      "Insulin                     0\n",
      "BMI                         0\n",
      "DiabetesPedigreeFunction    0\n",
      "Age                         0\n",
      "dtype: int64\n",
      "     Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
      "0      148.0           72.0           35.0    125.0  33.6   \n",
      "1       85.0           66.0           29.0    125.0  26.6   \n",
      "2      183.0           64.0           29.0    125.0  23.3   \n",
      "3       89.0           66.0           23.0     94.0  28.1   \n",
      "4      137.0           40.0           35.0    168.0  43.1   \n",
      "5      116.0           74.0           29.0    125.0  25.6   \n",
      "6       78.0           50.0           32.0     88.0  31.0   \n",
      "7      115.0           72.0           29.0    125.0  35.3   \n",
      "8      197.0           70.0           45.0    543.0  30.5   \n",
      "9      125.0           96.0           29.0    125.0  32.3   \n",
      "10     110.0           92.0           29.0    125.0  37.6   \n",
      "11     168.0           74.0           29.0    125.0  38.0   \n",
      "12     139.0           80.0           29.0    125.0  27.1   \n",
      "13     189.0           60.0           23.0    846.0  30.1   \n",
      "14     166.0           72.0           19.0    175.0  25.8   \n",
      "15     100.0           72.0           29.0    125.0  30.0   \n",
      "16     118.0           84.0           47.0    230.0  45.8   \n",
      "17     107.0           74.0           29.0    125.0  29.6   \n",
      "18     103.0           30.0           38.0     83.0  43.3   \n",
      "19     115.0           70.0           30.0     96.0  34.6   \n",
      "20     126.0           88.0           41.0    235.0  39.3   \n",
      "21      99.0           84.0           29.0    125.0  35.4   \n",
      "22     196.0           90.0           29.0    125.0  39.8   \n",
      "23     119.0           80.0           35.0    125.0  29.0   \n",
      "24     143.0           94.0           33.0    146.0  36.6   \n",
      "25     125.0           70.0           26.0    115.0  31.1   \n",
      "26     147.0           76.0           29.0    125.0  39.4   \n",
      "27      97.0           66.0           15.0    140.0  23.2   \n",
      "28     145.0           82.0           19.0    110.0  22.2   \n",
      "29     117.0           92.0           29.0    125.0  34.1   \n",
      "..       ...            ...            ...      ...   ...   \n",
      "738     99.0           60.0           17.0    160.0  36.6   \n",
      "739    102.0           74.0           29.0    125.0  39.5   \n",
      "740    120.0           80.0           37.0    150.0  42.3   \n",
      "741    102.0           44.0           20.0     94.0  30.8   \n",
      "742    109.0           58.0           18.0    116.0  28.5   \n",
      "743    140.0           94.0           29.0    125.0  32.7   \n",
      "744    153.0           88.0           37.0    140.0  40.6   \n",
      "745    100.0           84.0           33.0    105.0  30.0   \n",
      "746    147.0           94.0           41.0    125.0  49.3   \n",
      "747     81.0           74.0           41.0     57.0  46.3   \n",
      "748    187.0           70.0           22.0    200.0  36.4   \n",
      "749    162.0           62.0           29.0    125.0  24.3   \n",
      "750    136.0           70.0           29.0    125.0  31.2   \n",
      "751    121.0           78.0           39.0     74.0  39.0   \n",
      "752    108.0           62.0           24.0    125.0  26.0   \n",
      "753    181.0           88.0           44.0    510.0  43.3   \n",
      "754    154.0           78.0           32.0    125.0  32.4   \n",
      "755    128.0           88.0           39.0    110.0  36.5   \n",
      "756    137.0           90.0           41.0    125.0  32.0   \n",
      "757    123.0           72.0           29.0    125.0  36.3   \n",
      "758    106.0           76.0           29.0    125.0  37.5   \n",
      "759    190.0           92.0           29.0    125.0  35.5   \n",
      "760     88.0           58.0           26.0     16.0  28.4   \n",
      "761    170.0           74.0           31.0    125.0  44.0   \n",
      "762     89.0           62.0           29.0    125.0  22.5   \n",
      "763    101.0           76.0           48.0    180.0  32.9   \n",
      "764    122.0           70.0           27.0    125.0  36.8   \n",
      "765    121.0           72.0           23.0    112.0  26.2   \n",
      "766    126.0           60.0           29.0    125.0  30.1   \n",
      "767     93.0           70.0           31.0    125.0  30.4   \n",
      "\n",
      "     DiabetesPedigreeFunction  Age  \n",
      "0                       0.627   50  \n",
      "1                       0.351   31  \n",
      "2                       0.672   32  \n",
      "3                       0.167   21  \n",
      "4                       2.288   33  \n",
      "5                       0.201   30  \n",
      "6                       0.248   26  \n",
      "7                       0.134   29  \n",
      "8                       0.158   53  \n",
      "9                       0.232   54  \n",
      "10                      0.191   30  \n",
      "11                      0.537   34  \n",
      "12                      1.441   57  \n",
      "13                      0.398   59  \n",
      "14                      0.587   51  \n",
      "15                      0.484   32  \n",
      "16                      0.551   31  \n",
      "17                      0.254   31  \n",
      "18                      0.183   33  \n",
      "19                      0.529   32  \n",
      "20                      0.704   27  \n",
      "21                      0.388   50  \n",
      "22                      0.451   41  \n",
      "23                      0.263   29  \n",
      "24                      0.254   51  \n",
      "25                      0.205   41  \n",
      "26                      0.257   43  \n",
      "27                      0.487   22  \n",
      "28                      0.245   57  \n",
      "29                      0.337   38  \n",
      "..                        ...  ...  \n",
      "738                     0.453   21  \n",
      "739                     0.293   42  \n",
      "740                     0.785   48  \n",
      "741                     0.400   26  \n",
      "742                     0.219   22  \n",
      "743                     0.734   45  \n",
      "744                     1.174   39  \n",
      "745                     0.488   46  \n",
      "746                     0.358   27  \n",
      "747                     1.096   32  \n",
      "748                     0.408   36  \n",
      "749                     0.178   50  \n",
      "750                     1.182   22  \n",
      "751                     0.261   28  \n",
      "752                     0.223   25  \n",
      "753                     0.222   26  \n",
      "754                     0.443   45  \n",
      "755                     1.057   37  \n",
      "756                     0.391   39  \n",
      "757                     0.258   52  \n",
      "758                     0.197   26  \n",
      "759                     0.278   66  \n",
      "760                     0.766   22  \n",
      "761                     0.403   43  \n",
      "762                     0.142   33  \n",
      "763                     0.171   63  \n",
      "764                     0.340   27  \n",
      "765                     0.245   30  \n",
      "766                     0.349   47  \n",
      "767                     0.315   23  \n",
      "\n",
      "[768 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "#怀孕次数这个特征以外的特征的缺失值处理，用中值填补\n",
    "mid_train = mid_train.drop('Pregnancies', axis = 1)\n",
    "mid_train = mid_train.replace(0, np.NaN)\n",
    "mid_train = mid_train.fillna(mid_train.median())\n",
    "print(mid_train.isnull().sum())\n",
    "print(mid_train)#查看一下缺失值是否已经填充"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "缺失值插值后，现在再把所有数据合并在一齐"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:11:28.764943Z",
     "start_time": "2018-10-18T05:11:28.736941Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "New_train_set = train.loc[:,['Pregnancies']]#此句的目的是想将Pregnancies这一列放回第一列\n",
    "New_train_set = New_train_set.join(mid_train)\n",
    "New_train_set = New_train_set.join(train.loc[:,['Outcome']])#再把输出Outcome这一列并回去"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 保存为新的特征处理后的数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:11:28.843948Z",
     "start_time": "2018-10-18T05:11:28.771943Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "New_train_set.to_csv('FE_Diabetes.csv', index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读入整理后的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:11:28.984956Z",
     "start_time": "2018-10-18T05:11:28.850948Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>121.656250</td>\n",
       "      <td>72.386719</td>\n",
       "      <td>29.108073</td>\n",
       "      <td>140.671875</td>\n",
       "      <td>32.455208</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>30.438286</td>\n",
       "      <td>12.096642</td>\n",
       "      <td>8.791221</td>\n",
       "      <td>86.383060</td>\n",
       "      <td>6.875177</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>18.200000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.750000</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>121.500000</td>\n",
       "      <td>27.500000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>125.000000</td>\n",
       "      <td>32.300000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  121.656250      72.386719      29.108073  140.671875   \n",
       "std       3.369578   30.438286      12.096642       8.791221   86.383060   \n",
       "min       0.000000   44.000000      24.000000       7.000000   14.000000   \n",
       "25%       1.000000   99.750000      64.000000      25.000000  121.500000   \n",
       "50%       3.000000  117.000000      72.000000      29.000000  125.000000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    32.455208                  0.471876   33.240885    0.348958  \n",
       "std      6.875177                  0.331329   11.760232    0.476951  \n",
       "min     18.200000                  0.078000   21.000000    0.000000  \n",
       "25%     27.500000                  0.243750   24.000000    0.000000  \n",
       "50%     32.300000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Train = pd.read_csv('FE_Diabetes.csv')\n",
    "Train.head(10)\n",
    "Train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练数据和测试数据分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:12:23.786090Z",
     "start_time": "2018-10-18T05:12:23.765089Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练样本的数目是： (614, 8)\n"
     ]
    }
   ],
   "source": [
    "X = Train.drop('Outcome', axis = 1)\n",
    "Y = Train['Outcome']\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2)\n",
    "print(\"训练样本的数目是：\",X_train.shape)#取80%后，训练样本的数目是614"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对数据进行标准化处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:12:25.183170Z",
     "start_time": "2018-10-18T05:12:25.170169Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "ss_X = StandardScaler()\n",
    "X_train = ss_X.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic回归模型训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 调用GridSearchCV来训练L1、L2正则模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:45:17.516981Z",
     "start_time": "2018-10-18T05:45:16.926947Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'penalty': ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='accuracy', verbose=0)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "Par_penalty = ['l1', 'l2']\n",
    "Par_C = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "Parameters = dict(penalty = Par_penalty, C = Par_C)\n",
    "LR = LogisticRegression()\n",
    "grid = GridSearchCV(LR, Parameters, cv = 5, scoring = 'accuracy')\n",
    "grid.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:45:19.501094Z",
     "start_time": "2018-10-18T05:45:19.457092Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\starwin\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\starwin\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\starwin\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\starwin\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\starwin\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\starwin\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\starwin\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.0026    , 0.00280018, 0.00180001, 0.00300021, 0.00300007,\n",
       "        0.00360003, 0.00400019, 0.00320015, 0.00340014, 0.0044003 ,\n",
       "        0.00520034, 0.0038002 , 0.00340028, 0.00260015]),\n",
       " 'mean_score_time': array([0.00100012, 0.00140004, 0.0012001 , 0.00120001, 0.00100007,\n",
       "        0.00100017, 0.00080004, 0.0012001 , 0.00080009, 0.00120006,\n",
       "        0.0012001 , 0.00120006, 0.0012001 , 0.00080009]),\n",
       " 'mean_test_score': array([0.64169381, 0.74104235, 0.71172638, 0.75732899, 0.77361564,\n",
       "        0.76547231, 0.76058632, 0.76058632, 0.75895765, 0.75895765,\n",
       "        0.75895765, 0.75895765, 0.75895765, 0.75895765]),\n",
       " 'mean_train_score': array([0.64169357, 0.74919362, 0.71173232, 0.76262812, 0.77403259,\n",
       "        0.77484394, 0.77443826, 0.77443661, 0.77402845, 0.77443661,\n",
       "        0.77484311, 0.77484311, 0.77484311, 0.77484311]),\n",
       " 'param_C': masked_array(data=[0.001, 0.001, 0.01, 0.01, 0.1, 0.1, 1, 1, 10, 10, 100,\n",
       "                    100, 1000, 1000],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_penalty': masked_array(data=['l1', 'l2', 'l1', 'l2', 'l1', 'l2', 'l1', 'l2', 'l1',\n",
       "                    'l2', 'l1', 'l2', 'l1', 'l2'],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'C': 0.001, 'penalty': 'l1'},\n",
       "  {'C': 0.001, 'penalty': 'l2'},\n",
       "  {'C': 0.01, 'penalty': 'l1'},\n",
       "  {'C': 0.01, 'penalty': 'l2'},\n",
       "  {'C': 0.1, 'penalty': 'l1'},\n",
       "  {'C': 0.1, 'penalty': 'l2'},\n",
       "  {'C': 1, 'penalty': 'l1'},\n",
       "  {'C': 1, 'penalty': 'l2'},\n",
       "  {'C': 10, 'penalty': 'l1'},\n",
       "  {'C': 10, 'penalty': 'l2'},\n",
       "  {'C': 100, 'penalty': 'l1'},\n",
       "  {'C': 100, 'penalty': 'l2'},\n",
       "  {'C': 1000, 'penalty': 'l1'},\n",
       "  {'C': 1000, 'penalty': 'l2'}],\n",
       " 'rank_test_score': array([14, 12, 13, 11,  1,  2,  3,  3,  5,  5,  5,  5,  5,  5]),\n",
       " 'split0_test_score': array([0.64227642, 0.73170732, 0.74796748, 0.77235772, 0.77235772,\n",
       "        0.78861789, 0.76422764, 0.76422764, 0.75609756, 0.75609756,\n",
       "        0.75609756, 0.75609756, 0.75609756, 0.75609756]),\n",
       " 'split0_train_score': array([0.64154786, 0.74745418, 0.70264766, 0.75967413, 0.77393075,\n",
       "        0.78004073, 0.77393075, 0.77189409, 0.77189409, 0.77393075,\n",
       "        0.77393075, 0.77393075, 0.77393075, 0.77393075]),\n",
       " 'split1_test_score': array([0.64227642, 0.7398374 , 0.6504065 , 0.74796748, 0.7804878 ,\n",
       "        0.7398374 , 0.74796748, 0.74796748, 0.74796748, 0.74796748,\n",
       "        0.74796748, 0.74796748, 0.74796748, 0.74796748]),\n",
       " 'split1_train_score': array([0.64154786, 0.74745418, 0.72708758, 0.76374745, 0.77800407,\n",
       "        0.77596741, 0.77800407, 0.78004073, 0.78004073, 0.78004073,\n",
       "        0.78004073, 0.78004073, 0.78004073, 0.78004073]),\n",
       " 'split2_test_score': array([0.64227642, 0.65853659, 0.65853659, 0.68292683, 0.70731707,\n",
       "        0.70731707, 0.71544715, 0.71544715, 0.71544715, 0.71544715,\n",
       "        0.71544715, 0.71544715, 0.71544715, 0.71544715]),\n",
       " 'split2_train_score': array([0.64154786, 0.76578411, 0.72505092, 0.77596741, 0.79429735,\n",
       "        0.78411405, 0.79022403, 0.79022403, 0.78818737, 0.79022403,\n",
       "        0.79022403, 0.79022403, 0.79022403, 0.79022403]),\n",
       " 'split3_test_score': array([0.64227642, 0.75609756, 0.73170732, 0.76422764, 0.77235772,\n",
       "        0.75609756, 0.75609756, 0.75609756, 0.75609756, 0.75609756,\n",
       "        0.75609756, 0.75609756, 0.75609756, 0.75609756]),\n",
       " 'split3_train_score': array([0.64154786, 0.75560081, 0.70672098, 0.76578411, 0.77393075,\n",
       "        0.77596741, 0.77596741, 0.77189409, 0.76985743, 0.76985743,\n",
       "        0.76985743, 0.76985743, 0.76985743, 0.76985743]),\n",
       " 'split4_test_score': array([0.63934426, 0.81967213, 0.7704918 , 0.81967213, 0.83606557,\n",
       "        0.83606557, 0.81967213, 0.81967213, 0.81967213, 0.81967213,\n",
       "        0.81967213, 0.81967213, 0.81967213, 0.81967213]),\n",
       " 'split4_train_score': array([0.64227642, 0.7296748 , 0.69715447, 0.74796748, 0.75      ,\n",
       "        0.75813008, 0.75406504, 0.75813008, 0.7601626 , 0.75813008,\n",
       "        0.7601626 , 0.7601626 , 0.7601626 , 0.7601626 ]),\n",
       " 'std_fit_time': array([4.89920847e-04, 7.48442917e-04, 3.99923339e-04, 6.32485107e-04,\n",
       "        6.32485089e-04, 1.01984222e-03, 1.50789149e-07, 4.00018735e-04,\n",
       "        4.89940339e-04, 2.41676554e-03, 9.79773560e-04, 7.48404698e-04,\n",
       "        4.89823538e-04, 4.89940316e-04]),\n",
       " 'std_score_time': array([6.32485089e-04, 4.89842988e-04, 7.48417431e-04, 4.00042545e-04,\n",
       "        6.32560492e-04, 1.50789149e-07, 4.00018706e-04, 3.99994861e-04,\n",
       "        4.00042545e-04, 4.00018706e-04, 4.00114074e-04, 7.48430176e-04,\n",
       "        3.99994861e-04, 4.00042545e-04]),\n",
       " 'std_test_score': array([0.00116999, 0.05152945, 0.04846924, 0.04419892, 0.04081108,\n",
       "        0.04386644, 0.03377043, 0.03377043, 0.03375159, 0.03375159,\n",
       "        0.03375159, 0.03375159, 0.03375159, 0.03375159]),\n",
       " 'std_train_score': array([0.00029142, 0.01186029, 0.01211055, 0.00908637, 0.01417053,\n",
       "        0.00888616, 0.01165821, 0.0105781 , 0.00949565, 0.01065623,\n",
       "        0.01004783, 0.01004783, 0.01004783, 0.01004783])}"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:45:30.607730Z",
     "start_time": "2018-10-18T05:45:30.591729Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7736156351791531\n",
      "{'C': 0.1, 'penalty': 'l1'}\n"
     ]
    }
   ],
   "source": [
    "#输出最好的模型参数 \n",
    "print(grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "得出最佳正则是L1，参数C为0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:54:13.994666Z",
     "start_time": "2018-10-18T05:54:13.092614Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\starwin\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\starwin\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
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89dRTx319+PDhx51Q+Ah/f/8TzldXl2qzJ24l0NKyrBTLsnxxD1T44Kh9fgWuALAsqw3g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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19ff5128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot CV误差曲线\n",
    "test_means = grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid.cv_results_[ 'std_test_score' ]\n",
    "train_means = grid.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "\n",
    "# plot results\n",
    "n_Cs = len(Par_C)\n",
    "number_penaltys = len(Par_penalty)\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Par_C)\n",
    "for i, value in enumerate(Par_penalty):\n",
    "    #pyplot.plot(log(Cs), test_scores[i], label= 'penalty:'   + str(value))\n",
    "    plt.errorbar(x_axis, test_scores[:,i], yerr=test_stds[:,i] ,label = Par_penalty[i] +' Test')\n",
    "    plt.errorbar(x_axis, train_scores[:,i], yerr=train_stds[:,i] ,label = Par_penalty[i] +' Train')\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'accuracy' )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上图可以看出在训练集上C越大（正则越少）的模型性能越好；测试集上也是在C=0.1时性能最好（L1正则和L2正则均是），测试集的标准差比训练集的大。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 线性SVM模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:16:21.641695Z",
     "start_time": "2018-10-18T05:16:19.804590Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
       "     verbose=0),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "SVC = LinearSVC()\n",
    "\n",
    "Par_C = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "Parameters = dict(C = Par_C)\n",
    "\n",
    "grid = GridSearchCV(SVC, Parameters, cv = 5)\n",
    "grid.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:16:21.692698Z",
     "start_time": "2018-10-18T05:16:21.659696Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\starwin\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\starwin\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\starwin\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\starwin\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\starwin\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\starwin\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\starwin\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.00280018, 0.0038003 , 0.01620097, 0.07560434, 0.07500434,\n",
       "        0.08280473, 0.07740445]),\n",
       " 'mean_score_time': array([0.00099998, 0.00120006, 0.00140004, 0.00100012, 0.00140009,\n",
       "        0.00079999, 0.00099993]),\n",
       " 'mean_test_score': array([0.75732899, 0.76221498, 0.75732899, 0.75570033, 0.76221498,\n",
       "        0.74918567, 0.63192182]),\n",
       " 'mean_train_score': array([0.76181428, 0.77280893, 0.7724016 , 0.77158694, 0.77280811,\n",
       "        0.74511864, 0.64909095]),\n",
       " 'param_C': masked_array(data=[0.001, 0.01, 0.1, 1, 10, 100, 1000],\n",
       "              mask=[False, False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'C': 0.001},\n",
       "  {'C': 0.01},\n",
       "  {'C': 0.1},\n",
       "  {'C': 1},\n",
       "  {'C': 10},\n",
       "  {'C': 100},\n",
       "  {'C': 1000}],\n",
       " 'rank_test_score': array([3, 1, 3, 5, 1, 6, 7]),\n",
       " 'split0_test_score': array([0.77235772, 0.77235772, 0.75609756, 0.75609756, 0.77235772,\n",
       "        0.7804878 , 0.70731707]),\n",
       " 'split0_train_score': array([0.75763747, 0.77393075, 0.77189409, 0.76985743, 0.76782077,\n",
       "        0.76171079, 0.71283096]),\n",
       " 'split1_test_score': array([0.74796748, 0.7398374 , 0.76422764, 0.76422764, 0.76422764,\n",
       "        0.72357724, 0.6504065 ]),\n",
       " 'split1_train_score': array([0.76374745, 0.77596741, 0.77800407, 0.77800407, 0.77596741,\n",
       "        0.73727088, 0.68228106]),\n",
       " 'split2_test_score': array([0.68292683, 0.70731707, 0.69918699, 0.69105691, 0.70731707,\n",
       "        0.69105691, 0.53658537]),\n",
       " 'split2_train_score': array([0.77800407, 0.78207739, 0.78818737, 0.78411405, 0.79226069,\n",
       "        0.77800407, 0.64562118]),\n",
       " 'split3_test_score': array([0.76422764, 0.75609756, 0.75609756, 0.75609756, 0.75609756,\n",
       "        0.75609756, 0.73170732]),\n",
       " 'split3_train_score': array([0.76374745, 0.77800407, 0.76985743, 0.77189409, 0.77189409,\n",
       "        0.71486762, 0.72301426]),\n",
       " 'split4_test_score': array([0.81967213, 0.83606557, 0.81147541, 0.81147541, 0.81147541,\n",
       "        0.79508197, 0.53278689]),\n",
       " 'split4_train_score': array([0.74593496, 0.75406504, 0.75406504, 0.75406504, 0.75609756,\n",
       "        0.73373984, 0.48170732]),\n",
       " 'std_fit_time': array([0.00074838, 0.0003999 , 0.00331074, 0.00776183, 0.0016734 ,\n",
       "        0.00567134, 0.00185473]),\n",
       " 'std_score_time': array([9.53674316e-08, 4.00137912e-04, 4.89940316e-04, 9.53674316e-08,\n",
       "        4.89901406e-04, 3.99994861e-04, 0.00000000e+00]),\n",
       " 'std_test_score': array([0.04419892, 0.0426126 , 0.03563549, 0.03833881, 0.03338878,\n",
       "        0.03785059, 0.08351869]),\n",
       " 'std_train_score': array([0.01038443, 0.00975153, 0.01116726, 0.01008177, 0.0117757 ,\n",
       "        0.02219432, 0.08792991])}"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:16:21.922711Z",
     "start_time": "2018-10-18T05:16:21.909710Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.762214983713355\n",
      "{'C': 0.01}\n"
     ]
    }
   ],
   "source": [
    "#输出最佳模型参数\n",
    "print(grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "得出最佳参数C为0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:16:13.021202Z",
     "start_time": "2018-10-18T05:16:12.421167Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\starwin\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\starwin\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
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goaZg/J1fJH7+ruelj4CKr82R2/2O2CcAjF8Nji392n5H1BHg7Hes+qpy7FH11bZufzCV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14966b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot CV误差曲线\n",
    "test_means = grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid.cv_results_[ 'std_test_score' ]\n",
    "train_means = grid.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "\n",
    "# plot results\n",
    "n_Cs = len(Par_C)\n",
    "test_scores = np.array(test_means)\n",
    "train_scores = np.array(train_means)\n",
    "test_stds = np.array(test_stds)\n",
    "train_stds = np.array(train_stds)\n",
    "\n",
    "x_axis = np.log10(Par_C)\n",
    "\n",
    "plt.errorbar(x_axis, test_scores, yerr = test_stds ,label = 'Test')\n",
    "plt.errorbar(x_axis, train_scores, yerr = train_stds ,label = 'Train')\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'accuracy' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "线性SVM得出来score与Logistic回归差不多，但C越大，SVM的score是反而越小的，就是效果越差，而Logistic回归是趋于恒定的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## RBF核SVM模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:21:56.814865Z",
     "start_time": "2018-10-18T05:21:37.047735Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "       fit_params=None, iid=True, n_jobs=1,\n",
       "       param_grid={'gamma': array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03]), 'C': array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03])},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "Par_C = np.logspace(-3, 3, 7)# logspace(a,b,N)把10的a次方到10的b次方区间分成N份 \n",
    "Par_Gamma = np.logspace(-3, 3,7)\n",
    "Parameters = dict(gamma = Par_Gamma, C = Par_C)\n",
    "SVC_rbf = SVC(kernel='rbf')\n",
    "grid_rbf = GridSearchCV(SVC_rbf, Parameters, cv = 5)\n",
    "grid_rbf.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:21:56.896870Z",
     "start_time": "2018-10-18T05:21:56.818866Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\starwin\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\starwin\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\starwin\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\starwin\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\starwin\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\starwin\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\starwin\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.02000122, 0.0210012 , 0.01880097, 0.01880112, 0.04180245,\n",
       "        0.05760336, 0.04900293, 0.01860094, 0.01820107, 0.01940117,\n",
       "        0.02360134, 0.06060343, 0.05940342, 0.04900274, 0.01920099,\n",
       "        0.018401  , 0.01840105, 0.02520142, 0.06460366, 0.05600324,\n",
       "        0.05360308, 0.018401  , 0.01660099, 0.01940112, 0.0310019 ,\n",
       "        0.06960416, 0.05900345, 0.05380306, 0.01700091, 0.01920104,\n",
       "        0.02840147, 0.03400187, 0.0800046 , 0.06620378, 0.0622036 ,\n",
       "        0.02080121, 0.03520203, 0.08020463, 0.035602  , 0.08200474,\n",
       "        0.07060409, 0.09420533, 0.03820214, 0.16600947, 0.23601341,\n",
       "        0.03700204, 0.07900443, 0.06620378, 0.05860329]),\n",
       " 'mean_score_time': array([0.00460019, 0.00440021, 0.00400023, 0.00420036, 0.00500021,\n",
       "        0.01140065, 0.01160069, 0.0044004 , 0.00400019, 0.00440021,\n",
       "        0.00440025, 0.00560026, 0.0120007 , 0.01120071, 0.00500045,\n",
       "        0.00400023, 0.00400019, 0.00480032, 0.00560036, 0.01060052,\n",
       "        0.01300077, 0.00400033, 0.00360017, 0.00320015, 0.00540023,\n",
       "        0.00540023, 0.01060052, 0.01260076, 0.0038003 , 0.00320015,\n",
       "        0.00320029, 0.00480046, 0.00520029, 0.0108006 , 0.01300073,\n",
       "        0.0032002 , 0.00340014, 0.00320005, 0.00480027, 0.00540018,\n",
       "        0.01220064, 0.01640105, 0.00360022, 0.00300016, 0.00320015,\n",
       "        0.0056004 , 0.00560045, 0.01060057, 0.01260071]),\n",
       " 'mean_test_score': array([0.64169381, 0.64169381, 0.64169381, 0.64169381, 0.64169381,\n",
       "        0.64169381, 0.64169381, 0.64169381, 0.64169381, 0.64169381,\n",
       "        0.64169381, 0.64169381, 0.64169381, 0.64169381, 0.64169381,\n",
       "        0.64332248, 0.752443  , 0.64169381, 0.64169381, 0.64169381,\n",
       "        0.64169381, 0.65472313, 0.76710098, 0.77035831, 0.69381107,\n",
       "        0.64169381, 0.64169381, 0.64169381, 0.77198697, 0.76221498,\n",
       "        0.76384365, 0.68078176, 0.64006515, 0.64169381, 0.64169381,\n",
       "        0.76547231, 0.76872964, 0.72149837, 0.67915309, 0.64006515,\n",
       "        0.64169381, 0.64169381, 0.76547231, 0.76547231, 0.67752443,\n",
       "        0.67915309, 0.64006515, 0.64169381, 0.64169381]),\n",
       " 'mean_train_score': array([0.64169357, 0.64169357, 0.64169357, 0.64169357, 0.64169357,\n",
       "        0.64169357, 0.64169357, 0.64169357, 0.64169357, 0.64169357,\n",
       "        0.64169357, 0.64169357, 0.64169357, 0.64169357, 0.64169357,\n",
       "        0.64861822, 0.77118375, 0.64169357, 0.64169357, 0.64169357,\n",
       "        0.64169357, 0.66654   , 0.77402845, 0.82084679, 0.96905933,\n",
       "        1.        , 1.        , 1.        , 0.77077145, 0.79153875,\n",
       "        0.88273724, 1.        , 1.        , 1.        , 1.        ,\n",
       "        0.77769361, 0.8159646 , 0.94462603, 1.        , 1.        ,\n",
       "        1.        , 1.        , 0.79235176, 0.84690858, 0.99185667,\n",
       "        1.        , 1.        , 1.        , 1.        ]),\n",
       " 'param_C': masked_array(data=[0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.01,\n",
       "                    0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.1, 0.1, 0.1, 0.1,\n",
       "                    0.1, 0.1, 0.1, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 10.0,\n",
       "                    10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 100.0, 100.0,\n",
       "                    100.0, 100.0, 100.0, 100.0, 100.0, 1000.0, 1000.0,\n",
       "                    1000.0, 1000.0, 1000.0, 1000.0, 1000.0],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_gamma': masked_array(data=[0.001, 0.01, 0.1, 1.0, 10.0, 100.0, 1000.0, 0.001,\n",
       "                    0.01, 0.1, 1.0, 10.0, 100.0, 1000.0, 0.001, 0.01, 0.1,\n",
       "                    1.0, 10.0, 100.0, 1000.0, 0.001, 0.01, 0.1, 1.0, 10.0,\n",
       "                    100.0, 1000.0, 0.001, 0.01, 0.1, 1.0, 10.0, 100.0,\n",
       "                    1000.0, 0.001, 0.01, 0.1, 1.0, 10.0, 100.0, 1000.0,\n",
       "                    0.001, 0.01, 0.1, 1.0, 10.0, 100.0, 1000.0],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'C': 0.001, 'gamma': 0.001},\n",
       "  {'C': 0.001, 'gamma': 0.01},\n",
       "  {'C': 0.001, 'gamma': 0.1},\n",
       "  {'C': 0.001, 'gamma': 1.0},\n",
       "  {'C': 0.001, 'gamma': 10.0},\n",
       "  {'C': 0.001, 'gamma': 100.0},\n",
       "  {'C': 0.001, 'gamma': 1000.0},\n",
       "  {'C': 0.01, 'gamma': 0.001},\n",
       "  {'C': 0.01, 'gamma': 0.01},\n",
       "  {'C': 0.01, 'gamma': 0.1},\n",
       "  {'C': 0.01, 'gamma': 1.0},\n",
       "  {'C': 0.01, 'gamma': 10.0},\n",
       "  {'C': 0.01, 'gamma': 100.0},\n",
       "  {'C': 0.01, 'gamma': 1000.0},\n",
       "  {'C': 0.1, 'gamma': 0.001},\n",
       "  {'C': 0.1, 'gamma': 0.01},\n",
       "  {'C': 0.1, 'gamma': 0.1},\n",
       "  {'C': 0.1, 'gamma': 1.0},\n",
       "  {'C': 0.1, 'gamma': 10.0},\n",
       "  {'C': 0.1, 'gamma': 100.0},\n",
       "  {'C': 0.1, 'gamma': 1000.0},\n",
       "  {'C': 1.0, 'gamma': 0.001},\n",
       "  {'C': 1.0, 'gamma': 0.01},\n",
       "  {'C': 1.0, 'gamma': 0.1},\n",
       "  {'C': 1.0, 'gamma': 1.0},\n",
       "  {'C': 1.0, 'gamma': 10.0},\n",
       "  {'C': 1.0, 'gamma': 100.0},\n",
       "  {'C': 1.0, 'gamma': 1000.0},\n",
       "  {'C': 10.0, 'gamma': 0.001},\n",
       "  {'C': 10.0, 'gamma': 0.01},\n",
       "  {'C': 10.0, 'gamma': 0.1},\n",
       "  {'C': 10.0, 'gamma': 1.0},\n",
       "  {'C': 10.0, 'gamma': 10.0},\n",
       "  {'C': 10.0, 'gamma': 100.0},\n",
       "  {'C': 10.0, 'gamma': 1000.0},\n",
       "  {'C': 100.0, 'gamma': 0.001},\n",
       "  {'C': 100.0, 'gamma': 0.01},\n",
       "  {'C': 100.0, 'gamma': 0.1},\n",
       "  {'C': 100.0, 'gamma': 1.0},\n",
       "  {'C': 100.0, 'gamma': 10.0},\n",
       "  {'C': 100.0, 'gamma': 100.0},\n",
       "  {'C': 100.0, 'gamma': 1000.0},\n",
       "  {'C': 1000.0, 'gamma': 0.001},\n",
       "  {'C': 1000.0, 'gamma': 0.01},\n",
       "  {'C': 1000.0, 'gamma': 0.1},\n",
       "  {'C': 1000.0, 'gamma': 1.0},\n",
       "  {'C': 1000.0, 'gamma': 10.0},\n",
       "  {'C': 1000.0, 'gamma': 100.0},\n",
       "  {'C': 1000.0, 'gamma': 1000.0}],\n",
       " 'rank_test_score': array([19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 18, 10,\n",
       "        19, 19, 19, 19, 17,  4,  2, 12, 19, 19, 19,  1,  9,  8, 13, 47, 19,\n",
       "        19,  5,  3, 11, 14, 47, 19, 19,  5,  5, 16, 14, 47, 19, 19]),\n",
       " 'split0_test_score': array([0.64227642, 0.64227642, 0.64227642, 0.64227642, 0.64227642,\n",
       "        0.64227642, 0.64227642, 0.64227642, 0.64227642, 0.64227642,\n",
       "        0.64227642, 0.64227642, 0.64227642, 0.64227642, 0.64227642,\n",
       "        0.64227642, 0.70731707, 0.64227642, 0.64227642, 0.64227642,\n",
       "        0.64227642, 0.65853659, 0.75609756, 0.73170732, 0.66666667,\n",
       "        0.64227642, 0.64227642, 0.64227642, 0.7804878 , 0.74796748,\n",
       "        0.73170732, 0.68292683, 0.64227642, 0.64227642, 0.64227642,\n",
       "        0.75609756, 0.74796748, 0.71544715, 0.69105691, 0.64227642,\n",
       "        0.64227642, 0.64227642, 0.74796748, 0.73170732, 0.6504065 ,\n",
       "        0.69105691, 0.64227642, 0.64227642, 0.64227642]),\n",
       " 'split0_train_score': array([0.64154786, 0.64154786, 0.64154786, 0.64154786, 0.64154786,\n",
       "        0.64154786, 0.64154786, 0.64154786, 0.64154786, 0.64154786,\n",
       "        0.64154786, 0.64154786, 0.64154786, 0.64154786, 0.64154786,\n",
       "        0.64154786, 0.77393075, 0.64154786, 0.64154786, 0.64154786,\n",
       "        0.64154786, 0.64765784, 0.77800407, 0.81466395, 0.96537678,\n",
       "        1.        , 1.        , 1.        , 0.77189409, 0.80244399,\n",
       "        0.88594705, 1.        , 1.        , 1.        , 1.        ,\n",
       "        0.77800407, 0.81670061, 0.94501018, 1.        , 1.        ,\n",
       "        1.        , 1.        , 0.80040733, 0.84521385, 0.99185336,\n",
       "        1.        , 1.        , 1.        , 1.        ]),\n",
       " 'split1_test_score': array([0.64227642, 0.64227642, 0.64227642, 0.64227642, 0.64227642,\n",
       "        0.64227642, 0.64227642, 0.64227642, 0.64227642, 0.64227642,\n",
       "        0.64227642, 0.64227642, 0.64227642, 0.64227642, 0.64227642,\n",
       "        0.63414634, 0.78861789, 0.64227642, 0.64227642, 0.64227642,\n",
       "        0.64227642, 0.64227642, 0.7804878 , 0.78861789, 0.71544715,\n",
       "        0.64227642, 0.64227642, 0.64227642, 0.7804878 , 0.76422764,\n",
       "        0.75609756, 0.69105691, 0.63414634, 0.64227642, 0.64227642,\n",
       "        0.77235772, 0.79674797, 0.67479675, 0.69105691, 0.63414634,\n",
       "        0.64227642, 0.64227642, 0.7804878 , 0.78861789, 0.67479675,\n",
       "        0.69105691, 0.63414634, 0.64227642, 0.64227642]),\n",
       " 'split1_train_score': array([0.64154786, 0.64154786, 0.64154786, 0.64154786, 0.64154786,\n",
       "        0.64154786, 0.64154786, 0.64154786, 0.64154786, 0.64154786,\n",
       "        0.64154786, 0.64154786, 0.64154786, 0.64154786, 0.64154786,\n",
       "        0.65173116, 0.76782077, 0.64154786, 0.64154786, 0.64154786,\n",
       "        0.64154786, 0.68431772, 0.76985743, 0.81262729, 0.97352342,\n",
       "        1.        , 1.        , 1.        , 0.76782077, 0.78615071,\n",
       "        0.87983707, 1.        , 1.        , 1.        , 1.        ,\n",
       "        0.77596741, 0.81059063, 0.93686354, 1.        , 1.        ,\n",
       "        1.        , 1.        , 0.78818737, 0.84928717, 0.9898167 ,\n",
       "        1.        , 1.        , 1.        , 1.        ]),\n",
       " 'split2_test_score': array([0.64227642, 0.64227642, 0.64227642, 0.64227642, 0.64227642,\n",
       "        0.64227642, 0.64227642, 0.64227642, 0.64227642, 0.64227642,\n",
       "        0.64227642, 0.64227642, 0.64227642, 0.64227642, 0.64227642,\n",
       "        0.65853659, 0.70731707, 0.64227642, 0.64227642, 0.64227642,\n",
       "        0.64227642, 0.65853659, 0.69918699, 0.73170732, 0.72357724,\n",
       "        0.64227642, 0.64227642, 0.64227642, 0.70731707, 0.69918699,\n",
       "        0.74796748, 0.66666667, 0.64227642, 0.64227642, 0.64227642,\n",
       "        0.70731707, 0.71544715, 0.73170732, 0.65853659, 0.64227642,\n",
       "        0.64227642, 0.64227642, 0.70731707, 0.73170732, 0.65853659,\n",
       "        0.65853659, 0.64227642, 0.64227642, 0.64227642]),\n",
       " 'split2_train_score': array([0.64154786, 0.64154786, 0.64154786, 0.64154786, 0.64154786,\n",
       "        0.64154786, 0.64154786, 0.64154786, 0.64154786, 0.64154786,\n",
       "        0.64154786, 0.64154786, 0.64154786, 0.64154786, 0.64154786,\n",
       "        0.66598778, 0.78615071, 0.64154786, 0.64154786, 0.64154786,\n",
       "        0.64154786, 0.69857434, 0.78207739, 0.83299389, 0.97556008,\n",
       "        1.        , 1.        , 1.        , 0.78207739, 0.80448065,\n",
       "        0.88391039, 1.        , 1.        , 1.        , 1.        ,\n",
       "        0.78818737, 0.82688391, 0.9490835 , 1.        , 1.        ,\n",
       "        1.        , 1.        , 0.80448065, 0.84317719, 0.99389002,\n",
       "        1.        , 1.        , 1.        , 1.        ]),\n",
       " 'split3_test_score': array([0.64227642, 0.64227642, 0.64227642, 0.64227642, 0.64227642,\n",
       "        0.64227642, 0.64227642, 0.64227642, 0.64227642, 0.64227642,\n",
       "        0.64227642, 0.64227642, 0.64227642, 0.64227642, 0.64227642,\n",
       "        0.64227642, 0.7398374 , 0.64227642, 0.64227642, 0.64227642,\n",
       "        0.64227642, 0.67479675, 0.7398374 , 0.74796748, 0.70731707,\n",
       "        0.64227642, 0.64227642, 0.64227642, 0.74796748, 0.75609756,\n",
       "        0.76422764, 0.70731707, 0.64227642, 0.64227642, 0.64227642,\n",
       "        0.74796748, 0.74796748, 0.75609756, 0.70731707, 0.64227642,\n",
       "        0.64227642, 0.64227642, 0.75609756, 0.75609756, 0.70731707,\n",
       "        0.70731707, 0.64227642, 0.64227642, 0.64227642]),\n",
       " 'split3_train_score': array([0.64154786, 0.64154786, 0.64154786, 0.64154786, 0.64154786,\n",
       "        0.64154786, 0.64154786, 0.64154786, 0.64154786, 0.64154786,\n",
       "        0.64154786, 0.64154786, 0.64154786, 0.64154786, 0.64154786,\n",
       "        0.64154786, 0.78411405, 0.64154786, 0.64154786, 0.64154786,\n",
       "        0.64154786, 0.65784114, 0.78004073, 0.82281059, 0.97148676,\n",
       "        1.        , 1.        , 1.        , 0.77596741, 0.79226069,\n",
       "        0.88391039, 1.        , 1.        , 1.        , 1.        ,\n",
       "        0.78411405, 0.81873727, 0.9490835 , 1.        , 1.        ,\n",
       "        1.        , 1.        , 0.79226069, 0.85743381, 0.99185336,\n",
       "        1.        , 1.        , 1.        , 1.        ]),\n",
       " 'split4_test_score': array([0.63934426, 0.63934426, 0.63934426, 0.63934426, 0.63934426,\n",
       "        0.63934426, 0.63934426, 0.63934426, 0.63934426, 0.63934426,\n",
       "        0.63934426, 0.63934426, 0.63934426, 0.63934426, 0.63934426,\n",
       "        0.63934426, 0.81967213, 0.63934426, 0.63934426, 0.63934426,\n",
       "        0.63934426, 0.63934426, 0.86065574, 0.85245902, 0.6557377 ,\n",
       "        0.63934426, 0.63934426, 0.63934426, 0.8442623 , 0.8442623 ,\n",
       "        0.81967213, 0.6557377 , 0.63934426, 0.63934426, 0.63934426,\n",
       "        0.8442623 , 0.83606557, 0.7295082 , 0.64754098, 0.63934426,\n",
       "        0.63934426, 0.63934426, 0.83606557, 0.81967213, 0.69672131,\n",
       "        0.64754098, 0.63934426, 0.63934426, 0.63934426]),\n",
       " 'split4_train_score': array([0.64227642, 0.64227642, 0.64227642, 0.64227642, 0.64227642,\n",
       "        0.64227642, 0.64227642, 0.64227642, 0.64227642, 0.64227642,\n",
       "        0.64227642, 0.64227642, 0.64227642, 0.64227642, 0.64227642,\n",
       "        0.64227642, 0.74390244, 0.64227642, 0.64227642, 0.64227642,\n",
       "        0.64227642, 0.64430894, 0.7601626 , 0.82113821, 0.95934959,\n",
       "        1.        , 1.        , 1.        , 0.75609756, 0.77235772,\n",
       "        0.8800813 , 1.        , 1.        , 1.        , 1.        ,\n",
       "        0.76219512, 0.80691057, 0.94308943, 1.        , 1.        ,\n",
       "        1.        , 1.        , 0.77642276, 0.83943089, 0.99186992,\n",
       "        1.        , 1.        , 1.        , 1.        ]),\n",
       " 'std_fit_time': array([0.00209772, 0.00167348, 0.00074833, 0.00040002, 0.00426164,\n",
       "        0.00449913, 0.00209765, 0.00080001, 0.00039997, 0.00079997,\n",
       "        0.0008001 , 0.00233251, 0.00608644, 0.00089447, 0.00074834,\n",
       "        0.0004899 , 0.00048996, 0.00040002, 0.00233258, 0.00063249,\n",
       "        0.00287075, 0.00101984, 0.0004898 , 0.00101981, 0.00089458,\n",
       "        0.00691692, 0.00228039, 0.005231  , 0.0010955 , 0.00074839,\n",
       "        0.00162497, 0.00063241, 0.00340609, 0.00146972, 0.00305967,\n",
       "        0.00172057, 0.00263833, 0.00534456, 0.00135663, 0.00309856,\n",
       "        0.00392953, 0.01923019, 0.00203973, 0.02142079, 0.04799025,\n",
       "        0.00357785, 0.00189771, 0.00116623, 0.00185477]),\n",
       " 'std_score_time': array([4.89901382e-04, 4.89920847e-04, 6.32485107e-04, 4.00114074e-04,\n",
       "        1.09549632e-03, 1.85478467e-03, 1.20002429e-03, 4.89959789e-04,\n",
       "        1.50789149e-07, 4.89920871e-04, 4.89979265e-04, 8.00096994e-04,\n",
       "        1.78904461e-03, 7.48315479e-04, 1.09553984e-03, 1.78416128e-07,\n",
       "        1.50789149e-07, 4.00066404e-04, 8.00085089e-04, 4.90037648e-04,\n",
       "        1.89748040e-03, 2.43140197e-07, 4.89920871e-04, 4.00257111e-04,\n",
       "        4.89940316e-04, 4.89940339e-04, 4.89940316e-04, 2.05920836e-03,\n",
       "        4.00137912e-04, 4.00018706e-04, 4.00066376e-04, 4.00018706e-04,\n",
       "        3.99994861e-04, 7.48391947e-04, 2.09771572e-03, 4.00114074e-04,\n",
       "        4.89940316e-04, 4.00066376e-04, 7.48251775e-04, 4.89881921e-04,\n",
       "        1.93914289e-03, 3.72038377e-03, 4.89959789e-04, 1.16800773e-07,\n",
       "        4.00018706e-04, 4.89940316e-04, 4.89979265e-04, 4.89979242e-04,\n",
       "        2.24509934e-03]),\n",
       " 'std_test_score': array([0.00116999, 0.00116999, 0.00116999, 0.00116999, 0.00116999,\n",
       "        0.00116999, 0.00116999, 0.00116999, 0.00116999, 0.00116999,\n",
       "        0.00116999, 0.00116999, 0.00116999, 0.00116999, 0.00116999,\n",
       "        0.00817435, 0.04480972, 0.00116999, 0.00116999, 0.00116999,\n",
       "        0.00116999, 0.01282872, 0.05359213, 0.04588115, 0.02726615,\n",
       "        0.00116999, 0.00116999, 0.00116999, 0.04494739, 0.04671567,\n",
       "        0.02981158, 0.01810143, 0.00317171, 0.00116999, 0.00116999,\n",
       "        0.04471553, 0.04242188, 0.02678976, 0.02234687, 0.00317171,\n",
       "        0.00116999, 0.00116999, 0.04233028, 0.03413785, 0.02172705,\n",
       "        0.02234687, 0.00317171, 0.00116999, 0.00116999]),\n",
       " 'std_train_score': array([0.00029142, 0.00029142, 0.00029142, 0.00029142, 0.00029142,\n",
       "        0.00029142, 0.00029142, 0.00029142, 0.00029142, 0.00029142,\n",
       "        0.00029142, 0.00029142, 0.00029142, 0.00029142, 0.00029142,\n",
       "        0.00950358, 0.01519426, 0.00029142, 0.00029142, 0.00029142,\n",
       "        0.00029142, 0.02130104, 0.00807958, 0.0071733 , 0.00593162,\n",
       "        0.        , 0.        , 0.        , 0.00871909, 0.01169514,\n",
       "        0.00238832, 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.00888392, 0.00690385, 0.00452776, 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.0098294 , 0.0061506 , 0.00128811,\n",
       "        0.        , 0.        , 0.        , 0.        ])}"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_rbf.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:22:56.982307Z",
     "start_time": "2018-10-18T05:22:56.971306Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7719869706840391\n",
      "{'C': 10.0, 'gamma': 0.001}\n"
     ]
    }
   ],
   "source": [
    "#输出最好的模型参数 \n",
    "print(grid_rbf.best_score_)\n",
    "print(grid_rbf.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "得到最佳的参数C为10，gamma为0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-10-18T05:28:12.968380Z",
     "start_time": "2018-10-18T05:28:12.145333Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\starwin\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "C:\\Users\\starwin\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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dxrNt2zZKS0spLS2lsLAQgOXLl5vaSktL7+yEhUUapZxmlHK6sYchhHiASRFnwS5fvsyY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Qg8hW\naSMyJJL8DGkt4pWOZqjcIvul+sBqrwGgcGKayZEIcXaXj7+cK8ZfwW92/oZtx7aZHc6wIUmcEIPI\nWmllYeaT+hEJAAAgAElEQVRCIkJk2yOvHN4A7m7pD+cDq93JxFGxZCRIOxsR2FYsWkFWTBbLrctp\n6GgwO5xhQZI4IQbJocZDHGk6IlOpvigvBksYjF5sdiRBpb3LxSZHnaxKFUEhNjyWVUWrqGmt4ZH1\nj6C1NjukoCdJnBCDxFZpA5AkzheOYshZAOHRZkcSVLYeOkFHt1v6w4kzuv7ZEq5/tsTsMAaYmTaT\n78/9Pn8/9HfeLnvb7HC8cuibN3HomzeZHcYAfk3ilFKXKaX2KaXKlFLLz/D4U0qp7T23/Uqp+n6P\n3ayUsvfcbu53fL5SalfPcz6jlJLlWCIgWCusRmuROGkt4pW2eji6Q6ZSfVBsryEsRLEoN8XsUITw\n2K0zbmVRxiJ+vunnOBocZocT1PyWxCmlQoD/Ar4ATANuUEpN63+O1vpOrfUcrfUc4FngzZ5rk4EH\ngUXAQuBBpVRSz2XPAd8BJvbcLvPX1yCEp9q629hcvVk2vPfFoXWg3bKowQc2u5N5Y5KIiQg1OxQh\nPGZRFh4reIyIkAjuK76PTlen2SEFLX+OxC0EyrTWB7XWncDrwFXnOP8G4Pc9H/8L8JHWuk5rfQL4\nCLhMKZUJxGut12tjMv0l4Mv++xKE8Mzm6s10ujtlKtUXjmIIjTSmU4XHaps7KKlqlKlUEZTSY9J5\neOnDlNaV8qttvzI7nKDlzyQuGzjS7/OKnmOnUUqNBXKBT89zbXbPx54853eUUluUUltqamp8+gKE\n8JS1wkpUaBT56dJaxGuOYhizGEJlRa83bGW9W21JaxERnC4ZcwnXTb6Ol/a81FdTLLzjzyTuTLVq\nZ1uKcj3wJ6216zzXevycWusXtNb5Wuv8tDR5kxP+o7XGVmljUcYiwkPCzQ4nuLQ44XiJbLXlA5vd\nSUJUGDOzE8wORQif3Z1/N3mJedxvux9nm9PscIKOP5O4CqB/hXcOcLaN067n5FTqua6t6PnYk+cU\nYkiUN5ZT0VwhU6m+KLca97LpvVeMrbacLMtLIUS22hJBLDI0klVFq2jpauEna3+CW7vNDums9tbt\nZW/dXrPDGMCfSdxmYKJSKlcpFY6RqL176klKqclAErC+3+EPgX9WSiX1LGj4Z+BDrfVRoEkptbhn\nVepNwDt+/BqEOK++1iI5ksR5zVEM4XHGdlvCYwdqmjna0E5B3vCZZQjE9g1iaExMmsjd+Xdjq7Tx\naumrZocTVPyWxGmtu4HbMRKyUuANrXWJUuoRpdSV/U69AXhd9+v6p7WuA36GkQhuBh7pOQbwPeB/\ngDLgAPA3f30NQnjCVmljfMJ4smPPWJ4pzsVhhbFLIERWV3rDajemnWRRgxgurpt8HRePvpintj5F\naW2p2eEEDb/2idNav6+1nqS1nqC1fqzn2E+11u/2O+chrfVpPeS01v/3/9u78/i4qvqN458zk31r\ns7Vpk+4LBQptodDSjV1AZZFFAVEEFRFF/IGssrasRQVUXJBFsSyKKKCgorJkhtIVWqClkLRp2qTN\n3mZfJjPn98ekJS0tzTKTOzN53q9XmMzMnXufXNLJd8459xxr7cSurye6Pb7KWju1a5/f7178iQy0\nFl8LKytWqiu1Lxq2QW2R5ofrA29RDWOzUxiVpcmRJTYYY1g4ZyGZiZlcV3gdLb4WpyNFBa3YINIP\nKytW4gv4ND9cX5TsGg+nIq43OjoDLNtUq6W2JOZkJmVy9/y7KW0oZfHKxU7HiQoq4kT6wVMenFrk\niGFHOB0l+mwuhKShMPwwp5NElXe37KC5wx9T4+FEdpk1YhaXTr2U54ue59XNrzodJ+KpiBPpo91T\ni4zQ1CJ9UlIIY+eBS29DveEtrsHtMhwzQUttSWz63ozvMTV7Kre/fTvbm7Y7HSei6d1TpI9KGkoo\nbypnfr66Unttx2bYuUVTi/SBp6iGaQVDGJIc73QUkbCId8WzeMFi/AE/N3huwB/wH/hFg5SKOJE+\n8pQFx3SpiOuD3ePhdO56o77Fx3tlO7VKg8S8URmjuHn2zbxT9Q6PvP+I03Eiloo4kT7ylnuZMGQC\nI9JGOB0l+pQUQmou5E5xOklUWbqxhoCFBbqoQQaB0yeczhfGf4HfrP0N71a963SciKQiTqQPWnwt\nrK5cratS+8La4EoN4xaA0WoDveEpriEtMY5po4Y6HUVkQNw862ZGpo7khsIbaOhocDpOxFERJ9IH\ny7cvxxfwaX64vqgthsbtWi+1DzxF1cwen028W2/dMjikJaRx34L7qGypZNHbi9DUsHvSO4FIH3jL\nvaTEpWhqkb4oeTN4q/nheqW0tpmtda1apUF6bErWFKZkRf+QhcNzD+d707/Hvzb/ixeKX3A6TkRR\nESfSS9ZaPOUeZo+YTbxbVwj2WokHMgoga7zTSaKKltqSwezSqZdyVN5R3LPiHjbXb3Y6TsRQESfS\nS5vqN7G9ebsWvO+LQKBrPNz8qB4PZ30+2j7+mPaiIpo8HmwgEPZjeotqyB+azLic1LAfSyTSuF1u\n7pl3DwnuBK4rvA6f3+d0pIigIk6kl7zlXkBTi/RJ1XpoqY36rtS6J/9IoL4ef2MjW799GRtPOZXa\nxx6jc8eOsByv0x/grY01zJuYg4ni4lekP4anDmfhnIV8WPchP3/3507HiQgq4kR6yVPmYeLQieSl\n5jkdJfps7pofLoovavBt3071ww/jHjqU5OnTGfmTnxA3fBhV9/+E4mOPo/y662h5592QDsB+r7ye\nxrZO5k9WV6oMbieMPoGvHPQVfr/u9ywtX+p0HMepiBPphWZfM6urVqsVrq9KCiFzHAwd5XSSPqu8\n514IBIgfPRrjcjHki19g7JIljHvpRYaeey5N/3uN0gsvpOSsL7Hj2WfxNzX3+5jeohqMgbkTVMSJ\nXDPzGiYMmcBN3puoba11Oo6jVMSJ9MKy7cvoDHRqfri+CPhh81tR3ZXaVFhI46uvknP55bgSE/d4\nLmnyZPJuvYVJhW+Sd8cd4HJRcfsdFB97LNvvuIO2jz7u83E9RdVMHTmEzFSt0SuSHJfM4mMX09jR\nyC1v3TKopx1RESfSC95yL6nxqUwfNt3pKNFn+1por4/aIi7Q1kbFojtJGDeOrEsv2e92rtRUMr/y\nZcb99XnGPvsM6SedRP3zf6XkzDPZfOFXqf/7Pwh0dPT4uE3tnby7ZaeuShXpZnLmZK6ZeQ2ecg9P\nb3ja6TiOUREn0kPWWrzlXo4ZcQzxLk0t0mslhcHbKB0PV/u7R/Ft3UrerbfgSjhwi5gxJjhm7r57\nmfjmGwy79lo6a2rYdu21FB93PFU//SkdZWUH3M+yjbV0BizzVMSJ7OGCKRdwXMFx/HTVT/mo7iOn\n4zhCRZxIDxXvLKaiuUKrNPRVSWFwrdT04U4n6bWOzZup/d3vyPjCF0g95phevz4uM5Psb17KhH/9\nk1GPPkryETOofexxNp78ObZcdhmNr72O9fv3+VpPUTXJ8W6OHJPZ3x9DJKYYY1g4dyFDE4dybeG1\ntHa2Oh1pwKmIE+mhXVOLzM2f63CSKNTZAVuWRWUrnLWWijvvwsTHM+z66/q1L+NykTZvLqN++Usm\nvvY/cr77Xdo/3EDZFVdQfPLJ1PzmN3RWV+/xGk9xDUePyyIxzt2vY4vEosykTO6adxeb6zezeOVi\np+MMOBVxIj3kLfcyOXOyphbpi23vgK85KsfDNf77VZq9XnKv+gHxw4aFbL/xeXnk/uBKJr72P/If\neoiEMWOofvAhio4/gbL/+z+al6+gfEcLm6qbNR5O5DMcM/IYvjH1G/zl47/w39L/Oh1nQMU5HUAk\nGjR1NPFO5Tt8/dCvOx0lOpUUAgbGRldXtL+pmcp77iFxyhQyL7wwLMcw8fFknPI5Mk75HO2bStj5\npz+x84UXaPznv2gbOZozsmcwL29GWI4tEiuunH4lK7av4LaltzE1Z+qg+bCtljiRHli+fTmdtlPj\n4fqqpBDypkJKltNJeqXmV7+is7KSvNtuxcSF/zNv4vhxDL/xBia9+QYj7r6beuL57vsvwpe/yPZb\nbqF13bqwZxCJRvHueBYvWExnoJMbPTfiD+x7jGmsUREn0gOecg9p8WmaWqQvfK2wdQWMO9bpJL3S\n9vHH1P3hDww971xSZgxsS5grKYmMs87i6uOv4m+X38WQL36B+r//g83nnEvJl7/Czr/+jUBb24Bm\nEol0ozNG8+PZP2ZV5Soeff9Rp+MMCBVxIgdgrcVT7uGYkZpapE+2rgB/e1SNh7PWUnHHQtzp6eRe\nfbUjGdZvb6CuuYODjz2aEYsWManwTYbfdBOBpia233QTRcceR+U999JeUuJIPpFIdPr40zlt3Gn8\neu2vWVO1xuk4YaciTuQAinYWUdVSpaW2+mqzB4wbRvd+ag6n1L/wIq2rVzPsR9cQl+nM1B6FRcGr\nVOdODF7U4M7IIOvrX2P8y/9g9B/+QOqcY6h76ik2nfZ5Si+5hIZ/v4r1+RzJKhIpjDHcMvsW8lLz\nuMFzA40djU5HCisVcSIH4CkLLtquqUX6qKQQRs6ApAynk/SIf+dOqu6/n+Tp0xly9tmO5fAW1TAl\nL51h6Ul7PG6MIXXW0RQ88ACTXn+N3B9eRUdpKeVXXUXxiSdR/fNf4KuocCi1iPPSE9K5b8F9VDRX\nsGjZophelktFnMgBeMu9HJR5EMNSQje9xKDR3gTlq6OqK7XqwQfx79xJ3u23YVzOvEW2dvhZtXnH\nAacWicvNJefyy5n4n/9Q8KtfkTjlIGp+/WuKTzyJsiuvpOmtt7CBwAClFokc03KnccX0K/hnyT95\naeNLTscJGxVxIp+hsaORNVVrtOB9X21ZBoFOGBcd56/1vffY+ac/k/W1i0iaMsWxHMtLaunwB5g3\nKbdH2xu3m/QTjmf0I48w4dV/k33pJbSsWs3Wb36LjaedRu3jT9C5Y0eYU4tElm9O/SYzh8/kruV3\nUdpQ6nScsFARJ/IZlm1fpqlF+qPkTXDFw6jZTic5IOv3U3H7HcTl5JBz5ZWOZvEW1ZDgdnH02N5P\nyZIwahTDrrmGiW++wcj7FxOXnUPV4sUUH3sc266/gdY1a2K6e0lkF7fLzT3z7yHeFc91hdfh88fe\nmFEVcSKfwVvuJT0+nWm505yOEp02e2DU0ZCQ4nSSA9rx7LO0rV/P8BtvwJ2W5mgWb3ENR43LJDmh\n70ttuRISGHL66Yx9+inGvfgiQ845m8b//IfN519AydnnsONPfybQ3BzC1CKRJy81j4VzFrK+dj2/\nWPMLp+OEnIo4kf2w1uIt83LMyGOIc2lxk15r3QHb10bFeqmd1dVUP/gQqXOOIf200xzNUtXQxoaK\nRuZN7FlXak8kHTSZEbfdxsTCQvJuvw0CASpuu42iY4+jYtGdtBcVhexYIpHmxDEnct7k83jigyd4\ne9vbTscJKRVxIvvx8Y6PqWqtUldqX5UuBRuIiosaKu+/n0BbG8NvvgVjjKNZvMU1AGFZL9Wdlkrm\n+ecz7oW/Mebpp0k74Xh2/vnPbDr9DEov+hr1L7+M7egI+XFFnHbtUdcyYcgEbvLeRF1bndNxQkZF\nnMh+eMqDU4uoiOujEg/EJUHBTKeTfKbmFStoeOnvZH/zUhLHj3M6Dt6iGrJSEzhkRPimZDHGkHLE\nDPIXL2Zi4ZsMu/ZH+Cor2XbNjyg6/gSqfvYAHWXlYTu+yEBLjkvmvgX30dDewC1v3RIz40JVxIns\nh6fMw8FZB5ObErpurUGlpBBGz4a4RKeT7Jft6KBi4ULi8/PJ+c53nI4TXB2kuIa5E3NwuQamRTAu\nM5Psb36TCf/+F6N+9zuSp0+n9tFH2XjyyWz9zuU0vvEG1j841qGU2HZQ1kFcPfNqCssKeXrD007H\nCQkVcSL70NDRwNrqtWqF66vmGqhaF/FdqXVPPklH8UaG3/xjXMnJTsfho8pGqhvbmT8x9F2pB2Jc\nLtLmz2PUw79k4v/+S/bl36F1/TrKLv8uGz93CjWP/I7O2toBzyUSShdOuZAFBQv42aqf8VHdR07H\n6TcVcSL7sGzbMvzWr/nh+mpzsCuasZFbxPm2baP64V+RduKJpB9/vNNxgGBXKsC8MIyH6434ESMY\ndtVVTHrtNfIffID4ggKqf/Yzio47nvJrfkTLqlUx0x0lg4sxhkVzF5GRmMH1hdfT2tnqdKR+UREn\nsg+ecg/pCekclnOY01GiU0khJKQHl9uKUJX33APWknfTjU5H2c1TVMOE3FRGDnW+VRDAxMeTceqp\njPnD7xn/ystkXnA+TYWFlF70NUrOOIO6p57C39TkdEyRXslKyuKueXexsX4jP1n5E6fj9IuKOJG9\nWGvxlnuZM3KOphbpq5JCGDMH3JF5/hrfeIPG//yXnCuuID4/3+k4ALT5/CwvqWV+D1dpGGiJ48eT\nd9NNTCp8kxF33YlJTKJy0Z0ULTiW7bfeRtuHHzodUaTH5oycwyWHXsKfP/4z/yv9n9Nx+kxFnMhe\nNtRtoKa1hvn56krtk4ZtUFscsUttBdraqLzzLhLGjyf7Gxc7HWe3d0p30OYLMM+B8XC94UpOZug5\n5zDuL88x9rnnyDjtVOpfeomSL53N5q+cz84XXiDQ3u50TJEDunLGlRySfQi3vX0bFc0VTsfpExVx\nInvxlnsBmJs/1+EkUaqkazxchF7UUPvII/jKysi79VZMQoLTcXbzFNcQ5zLMnpDtdJQeSz5sKiPv\nuotJb77B8BtvwN/QwPYbbqR4wbFU3reYjtLYXK9SYkO8O57FCxbT4e/gJu9N+APRdxW2ijiRvXjL\nvRySfQg5yZHdIhKxSgohaSgMj7zxhO0lJdT+7lEyTj+d1NmznI6zB29RDUeMziQtMTK7oD+Le8gQ\nsi6+mPGvvMzo3z9ByuzZ1P3xj2w85VS2fPNbNP73v9jOTqdjinzKmIwx3DTrJlZWrOTxDx53Ok6v\nqYgT6aa+vZ411Ws0tUh/bC6EsfPAFVlvL9ZaKhctwiQmMvy6a52Os4e65g4+2Fbv+FWp/WWMIXX2\nbAoeepCJr/2PnB9cSfvGjZR9/0qKTzqZ6ocfxldZ5XRMkT2cOeFMTht7Gg+veZi11WudjtMrkfUu\nK+Kwt7e/TcAGNB6ur3Zshp1bYNyxTif5lMZ//YvmpW+T+8MfEpcbWRcPvFVcg7XhWWrLKfHDhpF7\nxRVM/O9/KHj4lyROnEjNL35J8QknUPaDq2h++21NUyIRwRjDLcfcQl5qHtcXXk9jR6PTkXpMRZxI\nN94yL0MSh2hqkb4qKQzeRth4OH9TE5V330PiIQeTecH5Tsf5FG9RDRlJcRxeMNTpKCFn4uJIP/FE\nRj/6Oya8+m+yvnExLStWsOWSS9l02uep/f3v8dfXOx1TBrn0hHTunX8vFc0V3Lnszqj5gKEiTqRL\nwAaCU4uMmIPb5XY6TnQq8UDqMMg9yOkke6j5xS/prKlhxG23YdyR9f/WWounqJo5E3JwD9BSW05J\nGD2a4ddey8Q332Dk4vtwZ2ZSde99FC04lvaSEvwNDXRs3kxndTWBlpao+UMqsWH6sOlcPu1yXil5\nhX9s+ofTcXok+kbQioTJhroN1LbVMq9A4+H6xNpgS9y4+WAipxhp++gj6pYsYeh555E8bZrTcT5l\nU00z2+rbuOL42OlKPRBXYiJDzjiDIWecQduGDex45ll2Pvcc/poaNp56WrcNXbhSUnClpuJKSwve\npgbvu1NTu+7v+krb6/6ntzPx8c790BIVvn3Yt1m2fRl3LruTabnTGJ0x2ulIn0lFnEgXT1lwaoy5\nIzW1SJ/UFEFTRUR1pdpAgIrb78CdkUHu//3Q6Tj7tGuprQUROslvuCVNmcKIO26nvagIf2MjOd/+\nFoHmZvxNTQSamwk0t3TdfvLlq62jvet5f0sL+Hw9OpZJTNyr0OtW7KWl4UrZ+7mur7TUTxeEycmY\nCPqwIqHhdrm5d/69nPPSOVxfeD1PnvYk8e7ILf5VxIl08ZZ7OTT7ULKTo2eeroiyuWs83NjIuSik\n/m8v0Pruu4y46y7iMjOdjrNPnqJqRmelMDo7xekojjJuN3FDhzLkjDN6/dpAR8enCr3A7iIw+OXf\n4/mW3c/7a+vwbdn6yXMtLT07qFoJY1Zeah63z7mdq9+4ml+u+SX/d+T/OR1pv1TEiRCcWuS9mve4\n7PDLnI4SvUoKIaMAssY7nQSAzh07qLr/fpKPOIIhXzrL6Tj75PMHWLapjjOmj3Q6SlRzJSTgSkiA\nEBTqNhAg0NJKoLnp04Vh96KwqWn/rYRdRaRaCaPXyWNO5pxJ5/DEB09wzMhjmD1ittOR9klFnAiw\ndNtSAjag+eH6KhCAzV6YdErEjIerfuBB/I2N5N12KybC5qzbZc3WnTS1d7IghqYWiXbG5cKdloo7\nLTUk+9tnK+FeLYWfaiXcVQT2s5XQtreDy0XxyZ8Lyc8y2HzVBjipBTp+8y0+Th3OiIomANo2bCBp\nyhSH0wWpiBMh2JU6NHEoU7OnOh0lOlWth5baiFkvtXXNGnY+9xxZF19M0kGRdaVsd56iGlwGjpmg\nIi5WhaeVsPkzWwr9zc0Emppp+Oc/wVqSZ0wPwU8yOGW3N+At91KXHCB/pxsDuFIiZ+iDijgZ9HZP\nLTJSU4v0WUnkjIeznZ1sX7iQuNxccr7/fafjfCZPUTWHFwxlSLLGQ8mB7dlKOOyA27dv2ABA/uLF\nYU4Wu/KB1euXcMvK+/hSVjxzi+NIGB05V6xGZh+DyAD6sPZD6trq1JXaH5s9wbFwQ0c5nYQdzzxL\n+/oPGX7TjSHrEguH+lYfa7fuVFeqSIT76sFfZV7+PP4+zcf2IQGn4+xBRZwMep5yDwbD3HxNLdIn\n/s7geLgIaIXzVVVR/dBDpM6dS/oppzgd5zO9vbGWgIV5g3RqEZFoYYzhzrl3ktwBS2Z30NbZ5nSk\n3VTEyaDnKfcwNWcqWUlZTkeJThVrob0hIuaHq1p8P7a9nbxbbo74q/M8RdWkJriZMTr2ltoSiTXZ\nydmcvyKBulTLutp1TsfZTUWcDGo723byfvX76krtj5LgJMlOt8Q1L1tGwz/+Qfa3v03C2LGOZukJ\nb3ENs8dnE+/W27BINDio0s2PX07iyOFHOh1lN717yKC2dNtSLJb5+c53BUatkkLInQLpwx2LYDs6\nqFi4iPiCArIv+7ZjOXpqS20LpbUtzNd4OJGoktYeWS38YS3ijDGnGmM+MsYUG2Nu2M82XzbGrDfG\nrDPGPN312PHGmDXdvtqMMWd1Pfd7Y0xJt+d07bT0mafcQ2ZiJofmHOp0lOjU2QFb3na8K7X293+g\nY9Mm8m65GVdSkqNZesJTXA1oPJyI9E/YphgxxriBh4GTgTJgpTHmJWvt+m7bTAJuBOZaa3cYY4YB\nWGtfB6Z3bZMFFAOvdtv9tdbav4QruwwOARtg6balzMmfg8uoUbpPtr0DvhZHu1J95eXU/OpXpJ98\nEmnHHutYjt7wFtUwYkgSE3Ij9+pZEYl84fzLdTRQbK3dZK3tAJ4Fztxrm28DD1trdwBYa6v2sZ9z\ngX9aa3s4VbVIz6yvXU9dW526UvujpBAwMNa5MYUVd98DxjD8xhsdy9Ab/oBl6cZa5k/KifiLL0Qk\nsoWziMsHtna7X9b1WHeTgcnGmLeMMcuMMafuYz/nA8/s9dhdxpj3jDEPGGMSQxdZBhNPWXBqkTkj\n5zgdJXqVFELeYZDizJW9ja+9TtP//kfu964gfmR0rD/6fnk99a0+daWKSL+Fs4jb10dMu9f9OGAS\ncBxwAfCoMWb39fbGmBHAYcC/u73mRmAKcBSQBVy/z4Mbc5kxZpUxZlV1dXVffwaJYd5yL4flHEZm\nUv+XwxmUfK2wdYVj4+ECra1U3nUXCRMnkPX1rzuSoS88Hwffj+ZOyHY4iYhEu3AWcWVA9+nbC4Bt\n+9jmRWutz1pbAnxEsKjb5cvA36y1vl0PWGu326B24AmC3bafYq19xFo701o7MzdXn3hlT3Vtdbxf\n8z7zCjS1SJ9tXQH+dseKuJrf/hZfeTl5t96KSUhwJENfeIprmJqfQXaaOhFEpH/CWcStBCYZY8YZ\nYxIIdou+tNc2LwDHAxhjcgh2r27q9vwF7NWV2tU6hwkOJjkL+CAs6SWmaWqRECgpBOOG0ccM+KHb\nN5VQ+9jjDDnzDFKP3ufnuIjU1N7Ju1t2MG+iPliKSP+F7epUa22nMeb7BLtC3cDj1tp1xpiFwCpr\n7Utdz33OGLMe8BO86rQWwBgzlmBL3pt77fopY0wuwe7aNcDl4foZJHZ5y71kJWVxSPYhTkeJXps9\nMHIGJGUM6GGttVQsWogrKYlh1147oMfur+WbavH5reaHE5GQCFsRB2CtfQV4Za/Hbu32vQWu7vra\n+7Wb+fSFEFhrTwh5UBlU/AE/b5W/xfz8+ZpapK/aG6F8Ncz5wYAfuuGVV2h5exnDb72FuJzoKoY8\nRTUkxrk4cozGYYpI/+kvmAw662rXsbN9p5ba6o8tyyDQOeDj4fyNjVTeey9Jhx5K5le+MqDHDgVv\ncQ2zxmeTFO92OoqIxAAVcTLoeMu9uIxLU4v0R0khuOJh1KwBPWz1L36Bv6aWvNtvw7ijqxDaXt9K\ncVUT8ydGV+uhiEQuFXEy6HjKPByWcxhDk4YeeGPZt5JCGHU0JKQM2CHbPvyQHUueYuj5XyH5sMMG\n7Lih4imqAWCexsOJSIioiJNBpba1lnW169SV2h+tO6DivQHtSrWBABW334F76FCG/fCHA3bcUPIW\n1ZCTlsiUvHSno4hIjFARJ4PK7qlFCjS1SJ+VLgUbGND1Uuv/+lda165l2LXX4h4yZMCOGyqBgOWt\n4hottSUiIaUiTgYVT7mHrKQsDs462Oko0aukEOKSoWDmgByuc8cOqu7/Cckzj2TIWXsvvxwd1m9v\noLa5g3kaDyciIaQiTgYNf8DP0m1LmZc/T1OL9EeJB0bPhriBWXGg+mc/w9/UFFyZIUpbsbzFGg8n\nIjyh4PUAAB/XSURBVKGnv2QyaLxf8z717fVapaE/mqqhah2MG5hz2PLuu+x87i9kXXwxSZMnD8gx\nw8FbVMNBw9MZnpHkdBQRiSEq4mTQ2DW1yDEjB36ZqJix2RO8HXds2A9lOzupuGMhccOHk/u9K8J+\nvHBp8/lZsblOrXAiEnJhXbFBJJJ4yj1My53GkMToGxgfMTZ7ICEdRkwP+6F2PP007Rs2kP/QQ7hS\nU8N+vHBZUVJHR2dARZyIhJxa4mRQqGmtYX3tek0t0l8lhTBmDrjD+/nPV1lF9UM/J3X+fNI/d3JY\njxVu3uIaEtwuZo3LcjqKiMQYFXEyKCzdthRARVx/NGyD2uIBmR+u6r77sD4feTf/OGovZtil8ONq\njhyTSUqCOj5EJLRUxMmg4CnzkJOcw5SsKU5HiV4lu8bDhfeihualS2l45RWyL7uMhDFjwnqscKtu\nbGdDRaO6UkUkLFTESczrDHSydNtS5o6cq6lF+qOkEJKGwvDwLXkV6OigYuEi4kePJvvb3wrbcQbK\nW11TiyyYlOtwEhGJRWrfl5j3Qc0HNHQ0aJWG/iopDLbCucJXCNc9/jgdmzcz6neP4EocmHnowslT\nVENmSjyHjsxwOoqIxCA1S0jMKywrxG3cmlqkP3ZshvotMDZ84+E6ysqo+fVvSD/lFNLmR37BvX57\nA+u3N+z3eWstnqJq5kzMweWK7nF9IhKZVMRJzPOWe5mWO42MBLWG9FlJYfA2jBc1VN51N7jdDL/x\nhrAdYyAVVTVR1djOfC21JSJhou5UiWk1rTV8WPchVx1xldNRoltJIaQOg9yDwrL7xtdeo+n11xl2\n7bXE5+WF5Rih9vvzrgfgtP087ynSUlsiEl5qiZOY5i33AppapF+sDV6ZOm4+hGG6j0BLC5V33kXi\npIlkff1rId+/UzxF1YzPSaUgM8XpKCISo9QSJzHNW+4lNzmXgzLD04I0KNQUQVNF2LpSa37zW3zb\ntjFmyR8x8fFhOcZAa+/0s3xTHefNLHA6iojEMLXESczaNbXIvPx5UT9hrKNK3gzehqGIa9+4kdon\nnmDIWWeRMnNmyPfvlHdKd9Lq8zNfU4uISBipiJOY9V71ezR2NKortb82eyCjADLHhXS31loqFi7C\nlZzMsGt/FNJ9O81TVI3bZZg9XkttiUj4qIiTmOUt92pqkf4KBLrGwy0I+Xi4hn+8TMvy5Qy7+v+I\ny84O6b6d5i2uYcaooaQnxUb3sIhEJhVxErM85R6mD5tOekK601GiV9V6aK0LeVeqv7GRyvvuI+mw\nwxh63nkh3bfTdjR38H55vbpSRSTsVMRJTKpqqWJD3QZ1pfbX7vnhQjv5bvVDP8dfW0vebbdh3O6Q\n7ttpSzfWYq2mFhGR8FMRJzHprfK3AJifH/kz/0e0kkLIGg9DQneVZeu6dex4+mkyL7iA5KmHhmy/\nkcJTVE16UhzTCoY4HUVEYpyKOIlJnnIPw1KGMTlzstNRope/E0rfCmlXqg0EqLhjIe7MTHJ/GHsT\nMAeX2qrhmPHZxLn19ioi4aV3GYk5voCPZduWMT9/vqYW6Y+KtdDeAGND15q587m/0Pbeewy//jrc\nGbG3DNrm2hbKd7Yyf7LGw4lI+GmyX4k5a6vW0ujT1CL9FuL1Ujvr6qj62c9IOeooMk4/PST7jDSe\nomoArZfaB2P++KTTEUSijlriJOZ4y73EmThmj5jtdJToVuKB3IMhbVhIdlf1058SaG4m79ZbYraF\n1FNUQ0FmMmOytdSWiISfijiJOd5yLzOGzyAtIc3pKNGrswO2vB2yq1Jb3nmH+uf/SvY3LiZx0qSQ\n7DPS+PwBlm2sZf6k3JgtUkUksqiIk5hS2VzJRzs+Uldqf5WvBl9LSLpSbWcnFbffQdyIEeR897sh\nCBeZ1m7dSWN7J/M1tYiIDBCNiZOY8ta24NQiKuL6abMHMDBmbr93VbdkCe0ff0z+L36OKzW1/9ki\nlKeoBmNgzoTYWn1CRCKXWuIkpnjKPAxPGc6kobHZZTdgSgoh7zBI6d/an77KSmp+/gtSj11A+kkn\nhShcZPIW13B4/hCGpiQ4HUVEBgkVcRIzfAEfy7YvY17+PI1J6g9fK2xdHpKu1Mp778X6/eTdfHNM\n/z9paPOxZutOLbUlIgNKRZzEjDVVa2jyNTG/QKs09MvWFeDv6HcR1+R9i8Z//ovs71xGwqhRIQoX\nmd7eWIs/YLXUlogMKBVxEjM85R7iXJpapN9KCsG4YfQxfd5FoL2dikULSRgzhuxvfjOE4SKTt6iG\nlAQ3R4zOdDqKiAwiurBBYoa33MsRw44gNT52B88PiJJCyD8Ckvq+okLtY4/hK93CqEcfxZWYGMJw\nkclbXMPs8dkkxOlzsYgMHL3jSEyoaK6gaEeRFrzvr/ZG2PZOv5ba6tiyhdrf/Jb0004lbV7/r26N\ndFvrWiipaWaeVmkQkQGmIk5igrfcC2hqkX7bsgwCnX0eD2etpeKuuzBxcQy/4YYQh4tM3uIaAM0P\nJyIDTkWcxARvuZe81DwmDJ3gdJToVvImuBNg1Kw+vbzxv/+l+c1Ccn5wJfHDh4c4XGTyFtWQl5HE\nxGFaIUREBpaKOIl6Pr+Pt7e9zfz8+TE9jcWAKPFAwVGQ0Pu1PwMtLVTefQ+JkyeTddFFYQgXeay1\neItrmDcpR797IjLgVMRJ1Hu36l1aOlvUldpfrTtg+9o+d6XW/PrXdG7fTt5tt2LiBsc1U80dfupb\nfepKFRFHDI53Wolp3nKvphYJhdKlgO1TEddeXEztE79nyNlnk3LkkaHPFqHqW30AzNVFDRKBxvzx\nSacjxJRnrzwUgFMcztGdWuIk6nnKPRw5/EhS4nvfBSjdlBRCXDLk964Is9ZSccdCXKmpDPvRNWEK\nF5nqW30cMiKDnLTYn0ZFRCKPijiJahXNFRTvLNbUIqFQUgijZ0Nc7wqShr//nZaVKxl29dXEZfVv\nrdVo4g9Ymto61ZUqIo5Rd6pENU+5B0BFXH81VUPVejjsvF69zN/QQOV9i0k6/HCGnndumMJFpsY2\nHxa01JbIIPHEqU84HeFTVMRJVPOUeRiZOpJxQ8Y5HSW6bQ4Ww70dD1f94EP4d+xg1CO/xbgGV8N+\nfasPY+CosYOn9VFEIsvgeteVmNLh72D59uXMy5+n6R36q6QQEtJhxPQev6T1g3XseOYZMi+8kORD\nDw1juMjS6Q/wrw8qqG3uID0xjqR4t9ORRGSQUkucRK13qt6hpbOF+QXqSu23zR4YOxfcPXtLsH4/\nFXfcgTs7m9yrfhDmcJGhsqGNZ1Zs4dkVW6loaCPB7WLk0GSnY4nIIKYiTqKWt8xLvCueo/OOdjpK\ndKsvh9piOPKSHr9k53PP0fb++4y8/37c6elhDOcsay1LN9ayZFkpr66vxB+wLJicy8IzD+VRzya1\nAIuIo1TESdTylnuZOXymphbpr16Oh+usraXqZw+QMmsWGV/8QhiDOae+xcdzq7fy9PItbKppJjMl\nnm/NG8eFs0YzJjsVgMe8JQ6nFJHBTkWcRKVtTdvYWL+Rsyed7XSU6FfigeRMGD61R5tX3f8TAq2t\n5N16S0y1RFlrWVtWz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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19806908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_means = grid_rbf.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid_rbf.cv_results_[ 'std_test_score' ]\n",
    "train_means = grid_rbf.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid_rbf.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "#先整理参数C与gamma对应的阵列\n",
    "n_Cs = len(Par_C)\n",
    "n_Gamma = len(Par_Gamma)\n",
    "test_scores = np.array(test_means).reshape(n_Cs,n_Gamma)\n",
    "train_scores = np.array(train_means).reshape(n_Cs,n_Gamma)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,n_Gamma)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs,n_Gamma)\n",
    "#先绘制不同参数C与Score关系图\n",
    "x_axis = np.log10(Par_C)\n",
    "for i, value in enumerate(Par_Gamma):\n",
    "     plt.errorbar(x_axis, test_scores[:,i], yerr=test_stds[:,i] ,label = 'gamma' + str(Par_Gamma[i]) + ' Test')\n",
    "     #plt.errorbar(x_axis, train_scores[:,i], yerr=train_stds[:,i], label = 'gamma' + str(Par_Gamma[i]) + ' Train')\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'accury' )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "gamma越小，对应的Score对高。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
